Main Effect of Heat Meta-Analysis
first get the data ready
#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
Warning: package ‘plyr’ was built under R version 4.1.2
top.table_H_intersect <- merge(top.table_Heat08,top.table_Heat12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_H_intersect)
[1] 12897 13
summary(top.table_H_intersect)
Gene logFC.08 AveExpr.08 t.08 P.Value.08
Length:12897 Min. :-6.20550 Min. :-2.530 Min. :-7.43821 Min. :0.0000
Class :character 1st Qu.:-0.35556 1st Qu.: 1.378 1st Qu.:-0.89703 1st Qu.:0.1560
Mode :character Median :-0.04893 Median : 3.343 Median :-0.12647 Median :0.3997
Mean : 0.05288 Mean : 3.261 Mean : 0.04085 Mean :0.4275
3rd Qu.: 0.36598 3rd Qu.: 4.970 3rd Qu.: 0.82476 3rd Qu.:0.6823
Max. : 9.81852 Max. :15.915 Max. :14.48736 Max. :1.0000
adj.P.Val.08 B.08 logFC.12 AveExpr.12 t.12
Min. :0.0000045 Min. :-6.775 Min. :-3.62522 Min. :-5.122 Min. :-10.731043
1st Qu.:0.6166042 1st Qu.:-6.226 1st Qu.:-0.18604 1st Qu.: 1.965 1st Qu.: -1.050162
Median :0.7947920 Median :-5.644 Median :-0.00171 Median : 3.609 Median : -0.009632
Mean :0.7295447 Mean :-5.383 Mean : 0.01721 Mean : 3.543 Mean : 0.059893
3rd Qu.:0.9075900 3rd Qu.:-5.051 3rd Qu.: 0.20145 3rd Qu.: 5.057 3rd Qu.: 1.055380
Max. :0.9999745 Max. :13.062 Max. : 9.30789 Max. :16.730 Max. : 11.517742
P.Value.12 adj.P.Val.12 B.12
Min. :0.00000 Min. :0.0000015 Min. :-6.687
1st Qu.:0.07103 1st Qu.:0.2893904 1st Qu.:-6.318
Median :0.30404 Median :0.6098134 Median :-5.783
Mean :0.36931 Mean :0.5586179 Mean :-5.055
3rd Qu.:0.63651 3rd Qu.:0.8477064 3rd Qu.:-4.678
Max. :0.99989 Max. :0.9998884 Max. :14.083
head(top.table_H_intersect)
#Put p-value and Fold changes results in lists
H_rawpval <- list("pvalue_08"=top.table_H_intersect[["P.Value.08"]], "pvalue_12"=top.table_H_intersect[["P.Value.12"]])
summary(H_rawpval)
Length Class Mode
pvalue_08 12897 -none- numeric
pvalue_12 12897 -none- numeric
H_adjpval <- list("adjpvalue_08"=top.table_H_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_H_intersect[["adj.P.Val.12"]])
summary(H_adjpval)
Length Class Mode
adjpvalue_08 12897 -none- numeric
adjpvalue_12 12897 -none- numeric
H_FC <- list("H_FC_08"=top.table_H_intersect[["logFC.08"]], "H_FC_12"=top.table_H_intersect[["logFC.12"]])
summary(H_FC)
Length Class Mode
H_FC_08 12897 -none- numeric
H_FC_12 12897 -none- numeric
#make matrix: 1 for genes DE at adjpval of 0.05, and 0 for not.
DE<- mapply(H_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_H_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)
DE.2008Dataset DE.2012Dataset
Min. :0.000000 Min. :0.00000
1st Qu.:0.000000 1st Qu.:0.00000
Median :0.000000 Median :0.00000
Mean :0.005815 Mean :0.08048
3rd Qu.:0.000000 3rd Qu.:0.00000
Max. :1.000000 Max. :1.00000
head(DE)
DE.2008Dataset DE.2012Dataset
A1CF 0 1
AAAS 0 0
AACS 0 1
AADAT 0 0
AAGAB 0 0
AAK1 0 0
dim(DE)
[1] 12897 2
#Check for normal distributions
par(mfrow = c(1,2))
hist(H_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(H_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")
Heat: Calculate the meta-analysis p-value
library(metaRNASeq)
# P-value combination using Fisher method
fishcomb <- fishercomb(H_rawpval, BHth = 0.05)
summary(fishcomb)
Length Class Mode
DEindices 1155 -none- numeric
TestStatistic 12897 -none- numeric
rawpval 12897 -none- numeric
adjpval 12897 -none- numeric
hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values (meta-analysis)")
# P-value combination using inverse normal method method
invnormcomb <- invnorm(H_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)
Length Class Mode
DEindices 1134 -none- numeric
TestStatistic 12897 -none- numeric
rawpval 12897 -none- numeric
adjpval 12897 -none- numeric
head(invnormcomb)
$DEindices
[1] 3 7 19 26 37 40 46 51 54 96 122 127 138 139 140
[16] 145 147 158 162 163 177 209 219 229 235 241 267 286 289 292
[31] 293 295 338 350 354 358 367 384 405 406 408 418 419 423 431
[46] 443 455 472 475 485 487 496 500 526 528 534 542 544 545 547
[61] 552 559 563 566 571 629 645 651 653 659 670 693 701 705 708
[76] 709 713 719 777 792 794 804 812 823 828 830 840 848 864 889
[91] 890 893 894 907 927 930 955 963 980 987 997 1002 1019 1027 1044
[106] 1087 1097 1107 1109 1134 1138 1157 1168 1179 1194 1200 1211 1212 1213 1223
[121] 1227 1245 1255 1260 1263 1267 1285 1286 1287 1291 1295 1310 1312 1325 1326
[136] 1334 1335 1384 1385 1388 1389 1428 1433 1439 1443 1474 1488 1492 1504 1522
[151] 1564 1566 1576 1597 1618 1626 1631 1636 1637 1643 1673 1680 1681 1685 1689
[166] 1694 1698 1702 1712 1729 1746 1749 1750 1751 1756 1766 1786 1790 1814 1826
[181] 1840 1842 1848 1849 1855 1876 1879 1880 1884 1889 1894 1895 1900 1919 1957
[196] 1972 1973 1981 1983 1989 2013 2014 2015 2022 2023 2045 2058 2092 2096 2117
[211] 2140 2153 2168 2196 2215 2224 2226 2227 2231 2242 2256 2283 2301 2314 2323
[226] 2336 2337 2340 2346 2368 2371 2378 2385 2390 2395 2404 2449 2453 2458 2459
[241] 2480 2508 2515 2516 2532 2534 2538 2583 2595 2598 2608 2611 2621 2626 2652
[256] 2653 2654 2660 2670 2681 2690 2700 2710 2745 2762 2766 2767 2770 2792 2839
[271] 2841 2843 2848 2870 2885 2889 2893 2894 2913 2914 2920 2931 2946 2951 2979
[286] 3000 3001 3009 3013 3028 3032 3036 3055 3059 3066 3068 3085 3094 3099 3100
[301] 3115 3116 3119 3122 3131 3144 3145 3157 3169 3171 3184 3201 3209 3232 3236
[316] 3252 3253 3255 3264 3266 3277 3328 3336 3342 3365 3367 3370 3385 3391 3402
[331] 3411 3418 3422 3428 3448 3449 3480 3489 3513 3514 3515 3520 3558 3559 3572
[346] 3573 3574 3580 3593 3595 3596 3597 3604 3619 3620 3642 3657 3660 3670 3673
[361] 3697 3704 3705 3706 3707 3708 3712 3716 3718 3723 3746 3747 3748 3784 3789
[376] 3813 3823 3830 3837 3857 3866 3877 3898 3925 3932 3944 3953 3974 3975 3981
[391] 3983 3985 3991 3998 4006 4007 4021 4025 4033 4053 4058 4078 4085 4087 4097
[406] 4099 4105 4116 4117 4119 4129 4131 4153 4156 4162 4163 4177 4192 4198 4199
[421] 4201 4207 4222 4235 4268 4269 4270 4286 4288 4334 4348 4358 4362 4390 4405
[436] 4430 4462 4469 4481 4501 4512 4517 4523 4554 4556 4570 4578 4591 4593 4600
[451] 4622 4623 4660 4673 4716 4728 4764 4776 4816 4819 4822 4823 4829 4834 4835
[466] 4839 4901 4916 4919 4933 4937 4959 4962 4993 4994 5004 5013 5017 5074 5110
[481] 5147 5165 5198 5200 5208 5238 5240 5249 5253 5273 5280 5291 5296 5305 5310
[496] 5322 5333 5335 5350 5353 5355 5378 5402 5403 5414 5415 5427 5433 5444 5467
[511] 5500 5508 5510 5529 5534 5566 5573 5577 5578 5601 5615 5629 5638 5660 5685
[526] 5691 5738 5746 5752 5760 5773 5791 5793 5800 5841 5848 5856 5874 5878 5881
[541] 5887 5901 5903 5912 5918 5927 5967 5972 5977 5987 6016 6031 6042 6072 6073
[556] 6082 6109 6184 6185 6186 6234 6244 6267 6282 6331 6338 6339 6342 6369 6371
[571] 6376 6378 6387 6402 6445 6463 6479 6481 6502 6504 6562 6677 6695 6707 6708
[586] 6751 6761 6773 6787 6788 6794 6798 6801 6804 6808 6809 6816 6877 6887 6892
[601] 6893 6908 6919 6935 6964 7003 7018 7029 7046 7050 7054 7071 7080 7081 7092
[616] 7129 7150 7152 7154 7167 7191 7212 7218 7220 7233 7261 7285 7292 7305 7340
[631] 7378 7383 7404 7412 7463 7477 7495 7496 7501 7508 7516 7522 7529 7532 7543
[646] 7560 7571 7582 7609 7615 7616 7617 7623 7625 7626 7627 7639 7659 7670 7671
[661] 7673 7674 7681 7694 7713 7729 7732 7743 7761 7802 7821 7824 7826 7836 7845
[676] 7860 7862 7863 7872 7890 7894 7905 7917 7920 7938 7977 8001 8003 8026 8069
[691] 8075 8116 8120 8129 8130 8135 8159 8169 8171 8182 8236 8239 8257 8260 8273
[706] 8275 8290 8307 8330 8341 8378 8405 8413 8439 8444 8448 8450 8452 8453 8461
[721] 8491 8496 8502 8517 8527 8534 8546 8561 8582 8594 8600 8608 8615 8636 8647
[736] 8648 8657 8663 8669 8674 8691 8695 8701 8705 8722 8723 8730 8738 8745 8747
[751] 8756 8763 8781 8789 8791 8813 8814 8816 8822 8827 8832 8837 8838 8869 8875
[766] 8909 8914 8952 8958 8959 8962 8983 9007 9013 9014 9031 9043 9051 9056 9067
[781] 9082 9086 9101 9111 9112 9135 9158 9162 9173 9184 9191 9220 9223 9225 9233
[796] 9238 9243 9256 9270 9276 9283 9298 9302 9305 9329 9354 9365 9386 9393 9395
[811] 9415 9421 9424 9425 9433 9434 9435 9499 9532 9537 9558 9565 9578 9586 9644
[826] 9653 9655 9668 9678 9711 9727 9740 9749 9797 9798 9807 9811 9814 9839 9847
[841] 9892 9903 9904 9905 9921 9922 9923 9932 9936 9937 9941 9943 9949 9951 9959
[856] 9963 9977 10001 10008 10013 10017 10033 10042 10049 10062 10063 10066 10069 10070 10092
[871] 10093 10095 10096 10111 10120 10121 10163 10165 10176 10255 10283 10295 10299 10304 10316
[886] 10324 10329 10343 10355 10356 10369 10380 10381 10383 10404 10431 10438 10455 10458 10500
[901] 10504 10515 10519 10562 10564 10573 10575 10576 10590 10591 10592 10604 10605 10623 10631
[916] 10642 10656 10663 10666 10667 10673 10677 10692 10698 10708 10732 10755 10763 10784 10799
[931] 10825 10843 10849 10850 10854 10871 10879 10889 10893 10904 10905 10912 10913 10916 10929
[946] 10936 10963 10964 10974 11019 11022 11032 11071 11078 11093 11094 11095 11107 11119 11121
[961] 11136 11138 11142 11147 11148 11166 11170 11174 11178 11186 11197 11218 11223 11226 11228
[976] 11229 11256 11275 11280 11295 11307 11314 11320 11323 11330 11331 11340 11348 11354 11375
[991] 11381 11393 11398 11411 11413 11434 11435 11476 11481 11503
[ reached getOption("max.print") -- omitted 134 entries ]
$TestStatistic
[1] 1.5242672484 1.7262237796 3.5820986228 1.4084765227 -1.2050770939 -0.0336611041
[7] 3.2309832996 -0.4572145454 2.1551642391 -0.4689016565 -1.3898159008 1.1866253829
[13] -0.7670811881 0.4815023496 0.5677909112 0.0384470189 1.8619326411 -0.0315418424
[19] 2.9107726197 -2.8640161332 0.3593292402 -0.9968829460 -0.8500806601 -0.8180861561
[25] 2.3070349908 2.6697495501 1.3544895361 2.2683501703 1.9296285274 0.7949690644
[31] 1.0969481286 -0.1254397239 0.7158455622 0.7532596797 -0.8120624336 0.6418425437
[37] 2.7112967859 -0.6590003850 0.1088571208 3.2431181015 0.9770916198 0.3587429538
[43] 0.2981832042 0.0627517752 1.0323099601 4.3549834180 2.3669157850 -1.3192965965
[49] 2.1359915180 1.6948709738 2.8755217351 0.4576200650 -0.0178145859 3.2530414529
[55] -0.6206782227 1.9738821419 -0.2381043130 1.9764922628 -0.4035902116 -0.4530967806
[61] 0.4041929956 0.6116462256 -0.7264583120 1.4444553273 0.2360642887 1.7105308488
[67] 1.5502790206 2.3603372877 -0.9259222202 0.6333501620 -0.1275907823 1.5562163704
[73] 2.2790889492 -0.7843555827 2.2881623141 1.7038220528 -0.7504809610 2.0705958050
[79] -0.3880240042 1.1838215494 -0.3809001296 -0.8618648212 -0.4122291910 1.1595707642
[85] 0.7150712567 0.7042314147 1.9010651662 -1.9744975878 1.3506344855 0.2429863590
[91] -0.9358462805 1.0672467034 -0.5602922714 0.0090263468 -1.1135616847 3.5505877743
[97] 0.4805297698 0.1118370863 -0.0994239166 -0.3484555646 1.8597666908 1.2175693751
[103] -0.3612091797 -0.6791801082 0.6433196158 1.5415494366 1.9297330829 -1.0302019094
[109] -0.2032479817 0.6631346597 1.4291274627 -0.5129638527 1.6268012880 -0.1707240490
[115] 0.2629608355 1.1646248547 -0.2661781774 0.3526757001 1.2769189581 -0.2384036895
[121] 1.3204901969 2.7379736051 -0.1096369566 0.5714650241 -0.5630555445 -0.1353570154
[127] 5.2581219844 0.4400788267 0.6108781812 0.3542465651 -0.4720329656 -2.7776052729
[133] -0.8453321515 -0.1259619819 -1.8022753405 0.8921801433 1.0423873609 2.9108393450
[139] 4.9227652564 3.7652130257 1.7587546818 0.9005193099 0.5122497702 0.1131812952
[145] 3.4433195132 0.8220894440 2.7008416101 0.8345145318 1.0902576364 0.6016312705
[151] 1.0006508037 2.1767182574 0.2934627708 0.8075604214 -1.7856257888 1.1025082742
[157] 2.0651863275 3.1004446548 1.9560519323 0.9880297448 1.6679811082 2.7485163330
[163] 3.5255456418 2.5835027925 0.0420567729 0.7753753308 1.4513411972 -2.5581424893
[169] 0.2188483501 1.4937134315 1.5931298728 -0.4669381442 -1.6022584314 1.5187803670
[175] 1.2933105321 -0.7100847106 3.1033458677 0.7820641909 -0.3040083159 -0.8680187156
[181] 1.1120983596 0.5764865406 0.5436425678 0.3895410096 1.4719940868 1.4804792192
[187] -1.1233857429 0.5866912464 1.5904481266 1.8407132235 -0.1399942415 0.2486524275
[193] -1.0642166495 -3.0914114487 0.3117866354 -0.3476216525 -0.4714139739 1.2008354025
[199] 0.4544187919 1.1624099257 -0.2235137055 -0.0313372023 -0.5485934967 1.1034559860
[205] -1.1982987114 1.4674154658 -0.6066397634 1.4967121143 2.9222185092 1.1921029425
[211] 1.0411825909 -0.7570288715 1.2021514914 0.3370241966 -0.2061644949 1.7967093731
[217] -0.3553297927 0.7014981430 3.5960826149 1.5024654772 1.9536544512 -0.3102448629
[223] 2.1590356379 0.5181533307 -0.8036915072 0.9359253383 2.5778995483 0.4369756491
[229] 3.9215041720 1.3494595373 -1.4200910876 -0.1097302726 0.1590894575 0.2285167800
[235] 2.8596265029 1.2871681817 -0.7399169901 0.5585237624 0.7934557360 -0.6441493554
[241] 4.6111938820 1.4611514697 0.9133326394 -0.5963105383 1.2808856361 0.0883213084
[247] 0.3919675203 1.2024592800 1.1656489254 2.1201171666 0.1998342586 1.4907619214
[253] -2.2731420742 1.5144608272 1.8463984271 1.9637224283 1.2102725295 1.0313476596
[259] 0.5086397714 0.4189169452 -0.1200568130 1.8462020564 0.8696690891 0.7885400874
[265] -2.2416606504 0.1710940117 7.7933447824 1.1784873654 -1.2727489904 -0.0902346190
[271] -2.0254259759 -1.9277818069 -0.8168280155 0.7020956097 -0.0421625078 0.5291053061
[277] -0.0756572810 0.3633123421 1.2130372364 -2.2018125322 0.8549898876 -0.8459256716
[283] 0.7504408858 -0.6755476310 -0.2515933484 2.8618895728 0.4743789681 0.0205431060
[289] 3.1376941286 2.1837495175 1.2465204457 7.5791926253 2.9810883659 -1.4390592113
[295] 3.8353987287 -0.4815333274 -0.9707442748 -0.9793805770 -0.2297194774 0.4247763404
[301] -1.2570874662 -0.5246368191 1.2347487360 -0.2558408525 0.5273510460 -0.6124061789
[307] -0.6565761810 -0.2467951946 1.4815526619 1.2568384195 1.2426220973 0.1853594766
[313] -0.2481667232 0.7117291279 -0.2709776801 1.3880648164 -1.1269270619 1.6692701084
[319] 0.7517595297 0.4021683123 -0.5621838518 -0.7099533758 -0.5987728730 0.5937780371
[325] -1.2384287548 -0.6881626922 1.4645232351 -1.3595142202 -0.2682838869 1.6245839725
[331] -0.1305089362 -1.8155146708 0.2209515460 -0.1690920044 -0.9244405780 -0.1163530477
[337] 2.4119183506 3.1017853272 1.0071541478 1.8614738632 0.8548099290 -1.4762391058
[343] -0.4825643364 2.3521069692 -0.7859322725 -0.5307354073 -0.3228725264 0.5643666787
[349] 0.2800694507 4.7118013474 0.9428244031 1.0325343516 -1.2010093689 3.2771620596
[355] 1.2517208402 0.2987870898 2.0231675306 3.2081767329 -0.7335595978 -1.9060809379
[361] 0.3641615864 2.0966033236 -1.1684765777 1.3484260689 2.2449016611 1.2645488098
[367] 5.8850156431 1.4374224626 1.2125246743 0.7558113575 0.2747543276 0.1017226584
[373] 0.6382205417 2.0583349301 -1.2505253185 -0.2562011391 -0.1739934055 0.4234593867
[379] 1.6275122123 0.1798871731 0.3293441221 -0.6650167276 -0.0143506550 3.9558022009
[385] -1.3447961547 0.8660860715 2.6172520535 2.3425625774 1.6127852727 1.0392767987
[391] 0.5100547294 -0.2849456026 0.0087581791 -0.3208397599 0.3944931199 -0.0234256993
[397] 0.3728551479 0.5398284604 -0.0694306843 0.4535968209 2.0313736135 0.5755421841
[403] 0.8255790194 1.0821688240 3.6461878534 5.3661542781 0.6989364818 3.4461151799
[409] -0.1542036159 0.5096470654 -0.1495241154 -1.2878211052 -2.0498926411 -0.3178429102
[415] 1.8230957103 0.7439076754 -1.2250562868 3.7318553615 2.9455676732 0.1321830812
[421] 0.2969212870 1.5934322553 2.8022947995 0.9848003533 -0.8725085214 2.1772474732
[427] 1.0238019943 -2.3513725723 1.0722289687 -1.3682020792 2.7749579250 -0.3700707306
[433] 1.7547450411 0.8164868768 0.2593750095 1.5657859660 0.0909235026 1.4435400388
[439] 1.7639161868 1.7266858018 -1.2268770995 1.1041398221 3.6960937912 -0.8038474217
[445] 2.0819242076 -0.6914512666 0.9290814673 0.5367482175 1.8197057546 0.0103594313
[451] 0.7813947148 0.5707599235 0.5784662226 -0.1602793496 3.8373620069 2.0407655280
[457] -0.2689595451 0.3723305051 -0.3134899511 0.2028870134 -0.2075449156 0.4401101218
[463] -0.1586313438 2.1975185916 0.1466044639 2.3911838391 1.3500673118 0.5434767008
[469] 2.3277173075 2.3127503419 -0.0004393409 3.3140116467 -2.2704077761 0.7841073465
[475] 3.4838302222 -1.0761409472 0.1574950180 1.0319876225 -1.3791153817 -0.2750057800
[481] -0.3321281575 -1.1201285257 -0.4743802467 -0.0622239997 2.8817957066 0.3574487748
[487] 2.6552315621 0.1842906021 2.0731632702 -0.3828627968 0.0237328429 -0.2498222730
[493] 1.2083631647 -1.7202200070 0.5625868078 3.3117835728 -0.8275737052 0.0922119343
[499] 1.7838254917 3.0299411667 1.6308552054 1.3412557825 -0.9237576356 -0.2630648180
[505] 1.5282652320 1.3333957779 -0.4819222948 1.7387456222 -1.0327233769 1.5035222889
[511] -0.5159003798 0.1381561455 -1.9033595309 -1.3701465598 -0.8711786096 -0.4430330495
[517] 0.0172208111 -0.7454230577 1.1671714703 0.6431793539 2.0009523383 -0.4173864689
[523] 1.8821291198 -0.3530842834 0.3148721280 3.7491166053 -0.7180773251 3.2764181801
[529] -0.3697686693 0.4640861803 1.2198162815 0.2135904504 2.5993843124 4.0242268463
[535] 0.1947714274 0.1777210842 1.5484086719 0.1662449625 0.9376290137 0.3307486496
[541] 1.8279526491 3.1327125146 0.4778766728 2.6998747613 3.4146793468 -0.9017757470
[547] 3.9812682211 1.6368057914 1.1920519475 1.2234021998 -0.2528851458 3.4702010379
[553] -1.4835976128 -0.6633393952 0.4711461782 0.5284361852 1.0910468216 0.0380180462
[559] 2.6781981154 -0.7122032623 -0.4594131247 -0.0003332294 4.1649186037 1.7673506888
[565] 0.0445133005 4.8304256148 0.9297600548 2.5732460489 1.3698044977 0.6591961085
[571] 3.4879071646 1.5895629236 0.7576196459 2.5195282439 1.0082957342 0.3373931630
[577] 2.0344840201 0.4153865658 2.2412418816 0.7747771026 1.8354413166 0.5840869002
[583] 0.6634017335 1.2364645175 0.2595131370 2.3009236435 0.2882070864 -0.0433417286
[589] 0.7531188720 1.0308069237 0.5618250708 0.1167565048 0.2094466456 -1.5292592225
[595] 1.5131755464 1.2665787438 2.0826845388 1.5027150046 1.4667867056 0.2115537977
[601] -1.3998516818 -0.2206688577 -0.4789751739 1.8346786521 0.0696350381 -0.8389539706
[607] 1.3078638457 0.5744731324 1.1158538861 0.2965933099 -0.2211920559 1.1142084036
[613] 1.3881456224 0.6974564180 0.7904848335 0.7180492628 0.7811756601 0.1645286836
[619] 0.2934746631 -0.9140745920 0.1272686443 0.4199540448 0.6570100962 0.1377249608
[625] 0.3580201829 -0.9832963829 -0.1830403628 1.0007701348 3.8176910870 1.9817261986
[631] 0.0368576191 1.0621912214 1.1258424934 -0.7071107612 -0.3219226067 -0.4109959844
[637] 1.0106883487 0.4474671276 0.4939577701 2.0698131986 0.0532651487 1.3101278265
[643] 1.0794041549 0.9675183492 3.7997180261 0.0819448075 0.8567163641 -2.2700793028
[649] -0.8118648961 -0.1375294226 3.3005126896 2.2033646730 3.1623448616 -0.3829344791
[655] -0.1529698433 0.9795227443 -0.4459396970 1.1499497426 2.7301016781 -0.3227515385
[661] 2.2802871464 0.6160572339 1.2683900540 -1.3606990820 1.3217844819 -1.3317355266
[667] -0.0359962045 1.0503669979 1.1082973602 2.8721319331 -0.7639272256 1.8109854394
[673] -0.2118105900 0.3096139480 1.1696678518 -1.3173903606 -0.8094567687 -0.1322238652
[679] 2.2308675381 1.1751752345 2.0064186130 1.2132498719 -0.7017427949 1.3241562809
[685] -0.3276305243 -0.8595684950 0.0119058746 -1.7937541732 1.5006149303 2.1155659111
[691] 1.1889708847 1.5333449188 3.2377166055 1.6711639439 0.2231234273 1.0531151748
[697] -0.1031697739 -0.2838255615 -0.4140844929 -0.3582569941 2.6234837976 0.7196896077
[703] 0.7638253497 1.4081237621 4.2974697081 -1.9762581174 -0.2002644191 2.7885026732
[709] 4.1462568611 2.0757183841 1.4163677779 1.9679199242 2.7380989368 0.3970901745
[715] 1.8290043926 -2.1534400728 -2.1151926464 -1.6979125761 3.5310387304 1.9969261765
[721] 0.1125300416 2.0284010554 -0.5557703347 -1.6651429283 1.7860319818 0.3381184979
[727] 0.8460707088 1.2696344474 -1.2607143310 0.0406926122 -1.5476760476 -0.9655201330
[733] -0.4245767820 0.0308865207 0.9370031457 -0.4101667035 -1.2504288648 -0.1220622063
[739] -0.7846119125 -0.1446410957 -0.1918033586 -0.8389943346 0.9362662861 -0.2506121842
[745] -0.1610191053 0.7638037605 2.0127603043 0.3817602690 -1.7479250559 -0.2702645535
[751] 1.3150919564 -1.4312668041 1.2704531962 0.1283992711 0.4581235872 0.5303051317
[757] 0.1036260650 0.1066459844 -0.5766848329 0.9433444173 1.4278253827 0.5176500574
[763] -1.2190474506 -0.2998867402 0.8007594061 -1.4237171131 0.3390588992 2.0045186677
[769] -0.4749772205 0.8101337198 -1.0329257500 0.0382935801 -1.2352582174 -0.5487330356
[775] -1.1729677666 1.2972197963 3.4408450020 -0.7168001549 0.8191197630 0.6083252347
[781] 1.8589702431 1.1438078193 1.9792033778 -0.9769346334 0.2370785619 -1.9047478259
[787] 1.2147151207 0.1156806566 -1.5118501586 0.6678364914 0.5032531103 5.5732223518
[793] -1.0984261027 4.3340053897 -0.9160635794 -0.0355974442 -0.4008850481 0.5272649433
[799] 0.6548294776 2.4400259021 0.3207878149 -0.5824952581 -0.5716515170 2.7449580175
[805] 0.6802924811 2.2287874389 1.2564115451 0.4093877681 2.5791083208 -1.3534490419
[811] -0.5457719797 2.9097739671 -1.0181273670 -0.6840027377 -1.4696869721 -0.3756718505
[817] 0.2037944373 0.4808951542 0.6697197071 0.9084775426 1.0877626839 0.0590282429
[823] 6.1484181880 2.4474704019 0.0062971672 -1.1412337674 -0.8103630247 4.4208672393
[829] 2.3704287472 3.7277468455 -1.2253342106 2.3999035301 0.0957547970 -0.2153972554
[835] -0.3130896916 1.6549193686 1.4187342660 1.6109581051 0.2565637624 2.7036393462
[841] 1.3735368611 -1.4600668508 0.4081937780 -1.9159529029 0.6506261406 -1.4627812229
[847] 0.5741703171 4.3979827502 1.3251556617 0.1935304420 1.8363557604 -2.0757205119
[853] 0.6577061082 2.5702780211 0.9481516228 1.6115192020 -0.5394512815 0.2943370515
[859] 2.5922695114 0.0533111227 1.6668387612 0.5842902150 -0.7500208625 2.8292299179
[865] -0.1054302339 0.0106168860 -0.8493130560 -0.0754458126 1.9209724861 1.0631388103
[871] 1.3741426294 -0.8277593099 2.5556431287 1.7117704646 0.0926252885 1.8358299360
[877] 1.3501786953 -0.9851432821 -0.6748523610 1.5760801638 -0.4175799442 0.0846685838
[883] 1.2955364700 0.6293358078 0.7247025751 -0.9689449935 -1.2644061078 0.2205226919
[889] 2.7413378943 3.6955137659 -0.1144501974 -1.0151101990 3.2138534221 3.2162946593
[895] 1.6185230554 -0.2903228953 0.0297226132 0.9278456711 0.3649239523 2.3215164983
[901] -0.6862582470 0.5513299268 0.5176971206 0.2471658947 0.0060814894 0.1018128491
[907] 2.8785472860 0.3180617921 1.7505215232 2.0291667546 -0.6167351978 1.7478233519
[913] -0.8861483925 -1.7181190662 1.2075157248 0.3548287853 2.4922841228 2.4203186876
[919] -0.1071528072 0.9500491036 0.5490453435 1.2776168230 2.4788603143 -0.8441774876
[925] -1.9772136646 -0.9565013206 2.8900290930 0.3569595791 -1.0168111060 3.4771100262
[931] -0.6469965083 0.4299611024 1.4409613799 -0.2084992868 0.3339133494 -1.4747635587
[937] -1.0782322051 1.0205615915 -0.4071907878 -0.2357199004 0.3754418293 -0.0509781405
[943] 1.2628641114 0.5569538319 1.5449447017 0.8876633153 1.2139911243 0.6983008989
[949] -0.4349511110 0.4799247888 2.2815593857 0.7392710591 2.3689346756 1.4092750167
[955] 3.1427117772 0.2796622670 0.3802129092 2.3816021044 -0.5932314756 0.6900152842
[961] -0.2803311070 0.9631560161 3.1749236974 -0.9833168642 -0.2898856128 -1.1147423020
[967] -0.6842575314 1.1790746546 2.1136755358 1.8391766513 1.5493180739 -1.2966553658
[973] -0.3879951726 1.1506913895 0.1217819161 -0.3752862941 0.0422538119 0.6793507035
[979] 0.0368792376 5.8761546827 -0.4713675529 -0.7396802114 1.2264478909 -0.8543442355
[985] 1.9486070060 -0.2607532260 3.0430348686 0.5933067651 0.4993094379 1.2184970095
[991] 0.9713785116 1.0079542911 1.2686490737 0.0840440063 -0.4458445133 -0.6414205682
[997] 3.1440465337 -0.7051725248 0.9792141334 0.4407613433
[ reached getOption("max.print") -- omitted 11897 entries ]
$rawpval
[1] 6.372098e-02 4.215358e-02 1.704225e-04 7.949501e-02 8.859132e-01 5.134263e-01
[7] 6.168257e-04 6.762416e-01 1.557449e-02 6.804300e-01 9.177076e-01 1.176877e-01
[13] 7.784834e-01 3.150798e-01 2.850885e-01 4.846656e-01 3.130629e-02 5.125813e-01
[19] 1.802681e-03 9.979085e-01 3.596744e-01 8.405893e-01 8.023599e-01 7.933460e-01
[25] 1.052643e-02 3.795392e-03 8.779012e-02 1.165394e-02 2.682644e-02 2.133157e-01
[31] 1.363320e-01 5.499123e-01 2.370434e-01 2.256469e-01 7.916221e-01 2.604877e-01
[37] 3.351030e-03 7.450522e-01 4.566579e-01 5.911460e-04 1.642619e-01 3.598937e-01
[43] 3.827817e-01 4.749821e-01 1.509635e-01 6.653850e-06 8.968507e-03 9.064650e-01
[49] 1.634005e-02 4.504995e-02 2.016802e-03 3.236127e-01 5.071066e-01 5.708842e-04
[55] 7.325943e-01 2.419757e-02 5.940999e-01 2.404953e-02 6.567430e-01 6.747605e-01
[61] 3.430354e-01 2.703859e-01 7.662211e-01 7.430547e-02 4.066914e-01 4.358388e-02
[67] 6.053728e-02 9.129163e-03 8.227568e-01 2.632525e-01 5.507636e-01 5.982832e-02
[73] 1.133089e-02 7.835843e-01 1.106404e-02 4.420717e-02 7.735175e-01 1.919829e-02
[79] 6.510009e-01 1.182419e-01 6.483613e-01 8.056190e-01 6.599143e-01 1.231118e-01
[85] 2.372825e-01 2.406443e-01 2.864674e-02 9.758374e-01 8.840627e-02 4.040080e-01
[91] 8.253238e-01 1.429302e-01 7.123600e-01 4.963991e-01 8.672664e-01 1.921860e-04
[97] 3.154254e-01 4.554763e-01 5.395992e-01 6.362510e-01 3.145927e-02 1.116938e-01
[103] 6.410285e-01 7.514881e-01 2.600084e-01 6.159156e-02 2.681996e-02 8.485424e-01
[109] 5.805294e-01 2.536222e-01 7.648380e-02 6.960117e-01 5.188965e-02 5.677796e-01
[115] 3.962904e-01 1.220854e-01 6.049490e-01 3.621658e-01 1.008154e-01 5.942160e-01
[121] 9.333570e-02 3.090952e-03 5.436513e-01 2.838422e-01 7.133015e-01 5.538352e-01
[127] 7.276699e-08 3.299400e-01 2.706401e-01 3.615771e-01 6.815484e-01 9.972619e-01
[133] 8.010373e-01 5.501190e-01 9.642490e-01 1.861482e-01 1.486161e-01 1.802297e-03
[139] 4.266489e-07 8.320357e-05 3.930959e-02 1.839220e-01 3.042381e-01 4.549434e-01
[145] 2.873100e-04 2.055130e-01 3.458213e-03 2.019956e-01 1.377998e-01 2.737098e-01
[151] 1.584978e-01 1.475080e-02 3.845842e-01 2.096718e-01 9.629201e-01 1.351204e-01
[157] 1.945269e-02 9.661516e-04 2.522952e-02 1.615690e-01 4.765974e-02 2.993283e-03
[163] 2.113057e-04 4.890134e-03 4.832267e-01 2.190590e-01 7.334244e-02 9.947384e-01
[169] 4.133841e-01 6.762527e-02 5.556553e-02 6.797279e-01 9.454508e-01 6.440889e-02
[175] 9.795184e-02 7.611742e-01 9.567294e-04 2.170884e-01 6.194392e-01 8.073080e-01
[181] 1.330479e-01 2.821432e-01 2.933437e-01 3.484380e-01 7.051124e-02 6.937270e-02
[187] 8.693631e-01 2.787055e-01 5.586691e-02 3.283180e-02 5.556677e-01 4.018148e-01
[193] 8.563847e-01 9.990040e-01 3.776013e-01 6.359378e-01 6.813274e-01 1.149075e-01
[199] 3.247637e-01 1.225345e-01 5.884321e-01 5.124997e-01 7.083578e-01 1.349146e-01
[205] 8.845996e-01 7.113153e-02 7.279550e-01 6.723409e-02 1.737738e-03 1.166104e-01
[211] 1.488954e-01 7.754837e-01 1.146524e-01 3.680493e-01 5.816688e-01 3.619089e-02
[217] 6.388287e-01 2.414961e-01 1.615226e-04 6.648847e-02 2.537105e-02 6.218126e-01
[223] 1.542370e-02 3.021756e-01 7.892124e-01 1.746558e-01 4.970144e-03 3.310645e-01
[229] 4.399896e-05 8.859470e-02 9.222094e-01 5.436884e-01 4.367992e-01 4.096223e-01
[235] 2.120701e-03 9.901784e-02 7.703248e-01 2.882434e-01 2.137562e-01 7.402607e-01
[241] 2.001815e-06 7.198694e-02 1.805338e-01 7.245161e-01 1.001169e-01 4.648107e-01
[247] 3.475411e-01 1.145928e-01 1.218782e-01 1.699808e-02 4.208051e-01 6.801201e-02
[253] 9.884912e-01 6.495450e-02 3.241719e-02 2.478115e-02 1.130872e-01 1.511889e-01
[259] 3.055024e-01 3.376384e-01 5.477809e-01 3.243144e-02 1.922406e-01 2.151904e-01
[265] 9.875083e-01 4.320749e-01 3.219647e-15 1.193012e-01 8.984464e-01 5.359496e-01
[271] 9.785882e-01 9.730589e-01 7.929866e-01 2.413098e-01 5.168154e-01 2.983662e-01
[277] 5.301541e-01 3.581858e-01 1.125578e-01 9.861607e-01 1.962784e-01 8.012029e-01
[283] 2.264946e-01 7.503360e-01 5.993223e-01 2.105618e-03 3.176148e-01 4.918051e-01
[289] 8.514124e-04 1.449033e-02 1.062867e-01 1.743050e-14 1.436129e-03 9.249331e-01
[295] 6.268038e-05 6.849313e-01 8.341622e-01 8.363040e-01 5.908451e-01 3.354999e-01
[301] 8.956390e-01 7.000822e-01 1.084620e-01 6.009631e-01 2.989749e-01 7.298655e-01
[307] 7.442733e-01 5.974666e-01 6.922968e-02 1.044061e-01 1.070036e-01 4.264735e-01
[313] 5.979973e-01 2.383163e-01 6.067959e-01 8.255865e-02 8.701133e-01 4.753193e-02
[319] 2.260978e-01 3.437801e-01 7.130046e-01 7.611335e-01 7.253378e-01 2.763303e-01
[325] 8.922214e-01 7.543248e-01 7.152552e-02 9.130082e-01 6.057596e-01 5.212562e-02
[331] 5.519181e-01 9.652776e-01 4.125651e-01 5.671379e-01 8.223715e-01 5.463136e-01
[337] 7.934417e-03 9.617870e-04 1.569304e-01 3.133864e-02 1.963282e-01 9.300601e-01
[343] 6.852974e-01 9.333703e-03 7.840464e-01 7.021989e-01 6.266041e-01 2.862523e-01
[349] 3.897121e-01 1.227683e-06 1.728854e-01 1.509109e-01 8.851262e-01 5.242808e-04
[355] 1.053358e-01 3.825513e-01 2.152794e-02 6.678969e-04 7.683914e-01 9.716801e-01
[361] 3.578687e-01 1.801435e-02 8.786927e-01 8.876069e-02 1.238722e-02 1.030166e-01
[367] 1.990082e-09 7.529899e-02 1.126558e-01 2.248812e-01 3.917525e-01 4.594884e-01
[373] 2.616651e-01 1.977900e-02 8.944461e-01 6.011022e-01 5.690647e-01 3.359801e-01
[379] 5.181418e-02 4.286206e-01 3.709478e-01 7.469801e-01 5.057249e-01 3.813913e-05
[385] 9.106545e-01 1.932215e-01 4.432043e-03 9.575911e-03 5.339558e-02 1.493380e-01
[391] 3.050066e-01 6.121571e-01 4.965060e-01 6.258341e-01 3.466085e-01 5.093446e-01
[397] 3.546281e-01 2.946577e-01 5.276766e-01 3.250595e-01 2.110855e-02 2.824623e-01
[403] 2.045215e-01 1.395888e-01 1.330797e-04 4.021657e-08 2.422959e-01 2.843540e-04
[409] 5.612754e-01 3.051494e-01 5.594300e-01 9.010959e-01 9.798125e-01 6.246980e-01
[415] 3.414445e-02 2.284662e-01 8.897230e-01 9.503732e-05 1.611813e-03 4.474197e-01
[421] 3.832633e-01 5.553163e-02 2.537024e-03 1.623611e-01 8.085345e-01 1.473105e-02
[427] 1.529644e-01 9.906479e-01 1.418086e-01 9.143756e-01 2.760442e-03 6.443351e-01
[433] 3.965146e-02 2.071109e-01 3.976730e-01 5.869937e-02 4.637767e-01 7.443420e-02
[439] 3.887305e-02 4.211205e-02 8.900656e-01 1.347662e-01 1.094711e-04 7.892575e-01
[445] 1.867470e-02 7.553590e-01 1.764234e-01 2.957208e-01 3.440191e-02 4.958673e-01
[451] 2.172852e-01 2.840812e-01 2.814747e-01 5.636695e-01 6.218154e-05 2.063707e-02
[457] 6.060196e-01 3.548234e-01 6.230458e-01 4.196117e-01 5.822078e-01 3.299287e-01
[463] 5.630203e-01 1.399172e-02 4.417221e-01 8.397071e-03 8.849720e-02 2.934008e-01
[469] 9.963560e-03 1.036818e-02 5.001753e-01 4.598384e-04 9.884086e-01 2.164886e-01
[475] 2.471465e-04 8.590679e-01 4.374274e-01 1.510390e-01 9.160704e-01 6.083441e-01
[481] 6.301038e-01 8.686705e-01 6.823856e-01 5.248078e-01 1.977080e-03 3.603779e-01
[487] 3.962699e-03 4.268927e-01 1.907854e-02 6.490893e-01 4.905329e-01 5.986376e-01
[493] 1.134538e-01 9.573038e-01 2.868581e-01 4.635162e-04 7.960440e-01 4.632648e-01
[499] 3.722601e-02 1.223007e-03 5.146044e-02 8.991871e-02 8.221937e-01 6.037497e-01
[505] 6.322335e-02 9.120098e-02 6.850694e-01 4.103976e-02 8.491333e-01 6.635221e-02
[511] 6.970380e-01 4.450585e-01 9.715032e-01 9.146794e-01 8.081717e-01 6.711291e-01
[517] 4.931302e-01 7.719920e-01 1.215706e-01 2.600539e-01 2.269876e-02 6.618021e-01
[523] 2.990924e-02 6.379874e-01 3.764294e-01 8.872928e-05 7.636452e-01 5.256638e-04
[529] 6.442226e-01 3.212930e-01 1.112673e-01 4.154332e-01 4.669558e-03 2.858140e-05
[535] 4.227859e-01 4.294710e-01 6.076197e-02 4.339821e-01 1.742175e-01 3.704172e-01
[541] 3.377833e-02 8.659950e-04 3.163690e-01 3.468279e-03 3.192857e-04 8.164120e-01
[547] 3.427427e-05 5.083553e-02 1.166204e-01 1.105889e-01 5.998215e-01 2.600345e-04
[553] 9.310421e-01 7.464434e-01 3.187682e-01 2.985983e-01 1.376261e-01 4.848366e-01
[559] 3.700971e-03 7.618305e-01 6.770312e-01 5.001329e-01 1.557318e-05 3.858476e-02
[565] 4.822476e-01 6.812075e-07 1.762477e-01 5.037478e-03 8.537397e-02 2.548849e-01
[571] 2.434085e-04 5.596668e-02 2.243394e-01 5.875611e-03 1.566563e-01 3.679103e-01
[577] 2.095141e-02 3.389295e-01 1.250521e-02 2.192357e-01 3.322016e-02 2.795809e-01
[583] 2.535366e-01 1.081430e-01 3.976197e-01 1.069797e-02 3.865941e-01 5.172854e-01
[589] 2.256892e-01 1.513157e-01 2.871176e-01 4.535265e-01 4.170498e-01 9.368999e-01
[595] 6.511754e-02 1.026530e-01 1.863999e-02 6.645628e-02 7.121704e-02 4.162276e-01
[601] 9.192211e-01 5.873249e-01 6.840219e-01 3.327666e-02 4.722421e-01 7.992524e-01
[607] 9.545975e-02 2.828238e-01 1.322423e-01 3.833885e-01 5.875286e-01 1.325949e-01
[613] 8.254635e-02 2.427586e-01 2.146223e-01 2.363635e-01 2.173496e-01 4.346575e-01
[619] 3.845797e-01 8.196612e-01 4.493639e-01 3.372595e-01 2.555872e-01 4.452289e-01
[625] 3.601641e-01 8.372692e-01 5.726168e-01 1.584690e-01 6.735322e-05 2.375495e-02
[631] 4.852993e-01 1.440744e-01 1.301161e-01 7.602512e-01 6.262443e-01 6.594623e-01
[637] 1.560828e-01 3.272689e-01 3.106680e-01 1.923492e-02 4.787603e-01 9.507630e-02
[643] 1.402038e-01 1.666425e-01 7.243041e-05 4.673453e-01 1.958008e-01 9.883986e-01
[649] 7.915654e-01 5.546938e-01 4.825417e-04 1.378453e-02 7.825205e-04 6.491158e-01
[655] 5.607890e-01 1.636609e-01 6.721796e-01 1.250823e-01 3.165740e-03 6.265583e-01
[661] 1.129533e-02 2.689284e-01 1.023293e-01 9.131956e-01 9.311996e-02 9.085264e-01
[667] 5.143573e-01 1.467747e-01 1.338667e-01 2.038564e-03 7.775447e-01 3.507155e-02
[673] 5.838726e-01 3.784273e-01 1.210673e-01 9.061461e-01 7.908738e-01 5.525964e-01
[679] 1.284495e-02 1.199623e-01 2.240580e-02 1.125172e-01 7.585802e-01 9.272557e-02
[685] 6.284045e-01 8.049865e-01 4.952504e-01 9.635738e-01 6.672759e-02 1.719087e-02
[691] 1.172256e-01 6.259545e-02 6.024522e-04 4.734465e-02 4.117197e-01 1.461441e-01
[697] 5.410859e-01 6.117280e-01 6.605939e-01 6.399245e-01 4.351779e-03 2.358581e-01
[703] 2.224857e-01 7.954721e-02 8.637943e-06 9.759372e-01 5.793631e-01 2.647616e-03
[709] 1.689773e-05 1.896000e-02 7.833393e-02 2.453863e-02 3.089774e-03 3.456505e-01
[715] 3.369948e-02 9.843579e-01 9.827932e-01 9.552379e-01 2.069656e-04 2.291660e-02
[721] 4.552016e-01 2.125967e-02 7.108161e-01 9.520579e-01 3.704704e-02 3.676369e-01
[727] 1.987566e-01 1.021074e-01 8.962941e-01 4.837705e-01 9.391498e-01 8.328578e-01
[733] 6.644274e-01 4.876800e-01 1.743785e-01 6.591582e-01 8.944285e-01 5.485751e-01
[739] 7.836594e-01 5.575029e-01 5.760519e-01 7.992638e-01 1.745681e-01 5.989430e-01
[745] 5.639608e-01 2.224921e-01 2.206993e-02 3.513196e-01 9.597615e-01 6.065216e-01
[751] 9.423950e-02 9.238231e-01 1.019616e-01 4.489165e-01 3.234318e-01 2.979502e-01
[757] 4.587331e-01 4.575349e-01 7.179238e-01 1.727524e-01 7.667106e-02 3.023512e-01
[763] 8.885869e-01 6.178682e-01 2.116355e-01 9.227358e-01 3.672827e-01 2.250726e-02
[769] 6.825984e-01 2.089317e-01 8.491807e-01 4.847268e-01 8.916328e-01 7.084057e-01
[775] 8.795956e-01 9.727779e-02 2.899503e-04 7.632513e-01 2.063590e-01 2.714859e-01
[781] 3.151568e-02 1.263517e-01 2.389656e-02 8.356992e-01 4.062979e-01 9.715936e-01
[787] 1.122374e-01 4.539528e-01 9.347140e-01 2.521190e-01 3.073932e-01 1.250350e-08
[793] 8.639908e-01 7.321029e-06 8.201832e-01 5.141983e-01 6.557476e-01 2.990048e-01
[799] 2.562888e-01 7.343104e-03 3.741856e-01 7.198834e-01 7.162210e-01 3.025932e-03
[805] 2.481596e-01 1.291403e-02 1.044834e-01 3.411276e-01 4.952786e-03 9.120439e-01
[811] 7.073887e-01 1.808451e-03 8.456913e-01 7.530133e-01 9.291767e-01 6.464196e-01
[817] 4.192571e-01 3.152955e-01 2.515182e-01 1.818130e-01 1.383499e-01 4.764648e-01
[823] 3.912973e-10 7.193147e-03 4.974878e-01 8.731137e-01 7.911342e-01 4.915278e-06
[829] 8.883734e-03 9.659965e-05 8.897753e-01 8.199697e-03 4.618577e-01 5.852712e-01
[835] 6.228937e-01 4.897043e-02 7.798825e-02 5.359443e-02 3.987578e-01 3.429234e-03
[841] 8.479276e-02 9.278641e-01 3.415657e-01 9.723145e-01 2.576439e-01 9.282364e-01
[847] 2.829263e-01 5.463083e-06 9.255976e-02 4.232718e-01 3.315253e-02 9.810401e-01
[853] 2.553635e-01 5.080846e-03 1.715261e-01 5.353330e-02 7.052122e-01 3.842502e-01
[859] 4.767252e-03 4.787420e-01 4.777324e-02 2.795125e-01 7.733789e-01 2.333008e-03
[865] 5.419828e-01 4.957646e-01 8.021464e-01 5.300700e-01 2.736759e-02 1.438595e-01
[871] 8.469870e-02 7.960966e-01 5.299588e-03 4.346949e-02 4.631006e-01 3.319141e-02
[877] 8.847934e-02 8.377232e-01 7.501152e-01 5.750366e-02 6.618729e-01 4.662624e-01
[883] 9.756761e-02 2.645646e-01 2.343173e-01 8.337137e-01 8.969578e-01 4.127321e-01
[889] 3.059477e-03 1.097213e-04 5.455595e-01 8.449733e-01 6.548326e-04 6.492871e-04
[895] 5.277496e-02 6.142154e-01 4.881441e-01 1.767438e-01 3.575841e-01 1.012949e-02
[901] 7.537249e-01 2.907038e-01 3.023348e-01 4.023899e-01 4.975739e-01 4.594526e-01
[907] 1.997557e-03 3.752190e-01 4.001418e-02 2.122065e-02 7.312953e-01 4.024731e-02
[913] 8.122312e-01 9.571126e-01 1.136168e-01 3.613589e-01 6.346223e-03 7.753455e-03
[919] 5.426661e-01 1.710437e-01 2.914872e-01 1.006923e-01 6.590146e-03 8.007149e-01
[925] 9.759913e-01 8.305905e-01 1.926031e-03 3.605610e-01 8.453784e-01 2.534249e-04
[931] 7.411829e-01 3.336120e-01 7.479780e-02 5.825804e-01 3.692225e-01 9.298619e-01
[937] 8.595349e-01 1.537311e-01 6.580661e-01 5.931750e-01 3.536660e-01 5.203285e-01
[943] 1.033190e-01 2.887795e-01 6.117982e-02 1.873609e-01 1.123756e-01 2.424945e-01
[949] 6.682010e-01 3.156404e-01 1.125768e-02 2.298712e-01 8.919702e-03 7.937693e-02
[955] 8.369529e-04 3.898683e-01 3.518937e-01 8.618756e-03 7.234869e-01 2.450923e-01
[961] 6.103883e-01 1.677346e-01 7.493795e-04 8.372742e-01 6.140481e-01 8.675196e-01
[967] 7.530937e-01 1.191842e-01 1.727149e-02 3.294460e-02 6.065264e-02 9.026251e-01
[973] 6.509902e-01 1.249296e-01 4.515359e-01 6.462762e-01 4.831482e-01 2.484578e-01
[979] 4.852906e-01 2.099530e-09 6.813109e-01 7.702530e-01 1.100151e-01 8.035429e-01
[985] 2.567119e-02 6.028586e-01 1.171026e-03 2.764879e-01 3.087807e-01 1.115176e-01
[991] 1.656799e-01 1.567382e-01 1.022831e-01 4.665107e-01 6.721452e-01 7.393753e-01
[997] 8.331447e-04 7.596486e-01 1.637371e-01 3.296929e-01
[ reached getOption("max.print") -- omitted 11897 entries ]
$adjpval
[1] 2.630632e-01 2.064773e-01 5.258228e-03 2.968925e-01 9.484211e-01 7.613315e-01
[7] 1.325867e-02 8.482287e-01 1.136584e-01 8.516385e-01 9.625630e-01 3.654752e-01
[13] 8.961450e-01 6.098764e-01 5.803133e-01 7.428057e-01 1.741445e-01 7.609947e-01
[19] 2.763425e-02 9.993281e-01 6.507591e-01 9.258759e-01 9.066090e-01 9.030700e-01
[25] 8.896424e-02 4.503144e-02 3.145955e-01 9.411447e-02 1.576940e-01 5.012998e-01
[31] 3.957403e-01 7.809953e-01 5.291433e-01 5.150826e-01 9.021428e-01 5.551909e-01
[37] 4.155612e-02 8.820395e-01 7.229950e-01 1.283503e-02 4.366136e-01 6.507591e-01
[43] 6.697511e-01 7.363264e-01 4.173879e-01 5.330105e-04 8.015720e-02 9.581739e-01
[49] 1.171423e-01 2.146927e-01 2.964809e-02 6.178584e-01 7.568747e-01 1.247914e-02
[55] 8.757317e-01 1.481140e-01 8.052541e-01 1.475288e-01 8.377857e-01 8.475790e-01
[61] 6.351030e-01 5.649064e-01 8.906104e-01 2.855459e-01 6.873964e-01 2.103672e-01
[67] 2.548969e-01 8.131134e-02 9.165669e-01 5.583239e-01 7.814917e-01 2.534842e-01
[73] 9.255896e-02 8.984021e-01 9.137384e-02 2.121845e-01 8.944755e-01 1.286684e-01
[79] 8.347683e-01 3.665782e-01 8.336074e-01 9.082451e-01 8.395891e-01 3.730670e-01
[85] 5.295054e-01 5.329881e-01 1.641024e-01 9.892645e-01 3.152142e-01 6.855909e-01
[91] 9.175806e-01 4.058501e-01 8.665733e-01 7.500148e-01 9.398080e-01 5.764238e-03
[97] 6.101756e-01 7.223501e-01 7.759182e-01 8.277721e-01 1.746258e-01 3.559390e-01
[103] 8.300546e-01 8.856427e-01 5.547275e-01 2.572365e-01 1.576940e-01 9.305826e-01
[109] 7.973912e-01 5.484515e-01 2.902918e-01 8.590739e-01 2.333406e-01 7.898747e-01
[115] 6.806442e-01 3.715816e-01 8.107161e-01 6.519905e-01 3.385505e-01 8.052541e-01
[121] 3.244611e-01 3.990391e-02 7.787037e-01 5.790435e-01 8.667278e-01 7.830936e-01
[127] 1.618062e-05 6.239349e-01 5.652543e-01 6.518394e-01 8.522772e-01 9.993281e-01
[133] 9.059877e-01 7.810052e-01 9.835431e-01 4.660752e-01 4.146909e-01 2.763425e-02
[139] 6.793199e-05 3.119408e-03 1.979391e-01 4.632894e-01 5.991386e-01 7.222311e-01
[145] 7.703611e-03 4.908593e-01 4.239683e-02 4.863959e-01 3.978519e-01 5.675739e-01
[151] 4.283626e-01 1.095859e-01 6.712224e-01 4.965365e-01 9.831278e-01 3.933742e-01
[157] 1.297215e-01 1.782612e-02 1.514123e-01 4.329479e-01 2.226250e-01 3.925895e-02
[163] 6.165632e-03 5.362930e-02 7.424451e-01 5.075842e-01 2.835493e-01 9.984246e-01
[169] 6.917626e-01 2.718713e-01 2.429250e-01 8.514259e-01 9.759467e-01 2.646838e-01
[175] 3.334460e-01 8.880021e-01 1.775387e-02 5.058337e-01 8.184518e-01 9.086647e-01
[181] 3.905401e-01 5.776791e-01 5.874849e-01 6.411647e-01 2.780802e-01 2.752922e-01
[187] 9.405399e-01 5.746097e-01 2.434997e-01 1.787386e-01 7.841609e-01 6.842099e-01
[193] 9.340176e-01 9.993281e-01 6.657450e-01 8.276265e-01 8.522044e-01 3.616851e-01
[199] 6.193118e-01 3.721920e-01 8.009410e-01 7.609947e-01 8.644439e-01 3.932189e-01
[205] 9.481161e-01 2.794012e-01 8.737646e-01 2.710591e-01 2.719855e-02 3.640895e-01
[211] 4.151111e-01 8.952214e-01 3.612686e-01 6.577154e-01 7.981746e-01 1.886636e-01
[217] 8.291328e-01 5.336832e-01 5.057867e-03 2.692509e-01 1.518356e-01 8.199332e-01
[223] 1.127661e-01 5.966984e-01 9.003231e-01 4.507669e-01 5.418424e-02 6.247789e-01
[229] 2.039641e-03 3.152886e-01 9.650872e-01 7.787037e-01 7.087820e-01 6.893925e-01
[235] 3.062786e-02 3.356026e-01 8.928623e-01 5.827677e-01 5.018775e-01 8.803266e-01
[241] 2.133670e-04 2.810575e-01 4.587871e-01 8.724635e-01 3.374444e-01 7.281742e-01
[247] 6.403483e-01 3.611690e-01 3.713355e-01 1.197907e-01 6.969934e-01 2.725141e-01
[253] 9.947387e-01 2.661958e-01 1.775707e-01 1.499026e-01 3.580424e-01 4.175339e-01
[259] 6.000707e-01 6.298124e-01 7.799976e-01 1.775707e-01 4.734251e-01 5.037776e-01
[265] 9.942927e-01 7.055155e-01 2.076189e-11 3.688869e-01 9.538413e-01 7.738549e-01
[271] 9.901037e-01 9.877670e-01 9.028203e-01 5.336372e-01 7.629772e-01 5.923690e-01
[277] 7.704599e-01 6.494746e-01 3.572272e-01 9.937116e-01 4.787379e-01 9.060161e-01
[283] 5.160044e-01 8.847215e-01 8.080637e-01 3.047829e-02 6.121158e-01 7.471233e-01
[289] 1.646277e-02 1.086522e-01 3.468569e-01 5.620029e-11 2.417985e-02 9.668223e-01
[295] 2.582712e-03 8.541053e-01 9.224217e-01 9.235757e-01 8.027949e-01 6.281855e-01
[301] 9.529166e-01 8.604666e-01 3.505521e-01 8.087886e-01 5.926201e-01 8.744148e-01
[307] 8.817649e-01 8.075496e-01 2.752309e-01 3.436607e-01 3.482273e-01 7.013213e-01
[313] 8.079216e-01 5.305653e-01 8.113890e-01 3.041300e-01 9.407991e-01 2.222695e-01
[319] 5.154496e-01 6.358002e-01 8.665826e-01 8.880021e-01 8.729689e-01 5.710354e-01
[325] 9.517767e-01 8.865113e-01 2.801289e-01 9.609161e-01 8.111609e-01 2.335257e-01
[331] 7.824600e-01 9.839697e-01 6.914687e-01 7.897402e-01 9.164543e-01 7.796433e-01
[337] 7.425993e-02 1.779651e-02 4.263600e-01 1.742131e-01 4.787379e-01 9.692918e-01
[343] 8.541053e-01 8.212183e-02 8.984021e-01 8.612705e-01 8.218563e-01 5.812937e-01
[349] 6.750997e-01 1.522445e-04 4.491271e-01 4.173879e-01 9.481915e-01 1.172922e-02
[355] 3.449154e-01 6.696205e-01 1.391011e-01 1.404209e-02 8.918620e-01 9.874524e-01
[361] 6.492379e-01 1.241748e-01 9.455392e-01 3.157313e-01 9.807122e-02 3.416529e-01
[367] 8.850374e-07 2.874071e-01 3.574035e-01 5.141451e-01 6.765035e-01 7.241870e-01
[373] 5.565812e-01 1.313026e-01 9.524954e-01 8.088160e-01 7.905588e-01 6.285371e-01
[379] 2.331375e-01 7.027612e-01 6.594230e-01 8.831173e-01 7.562617e-01 1.842248e-03
[385] 9.598780e-01 4.750243e-01 5.031695e-02 8.333369e-02 2.372177e-01 4.159083e-01
[391] 5.997480e-01 8.142458e-01 7.500148e-01 8.216820e-01 6.391493e-01 7.588100e-01
[397] 6.458118e-01 5.884484e-01 7.692709e-01 6.193371e-01 1.370781e-01 5.779655e-01
[403] 4.897351e-01 4.007739e-01 4.351530e-03 9.786286e-06 5.346261e-01 7.685868e-03
[409] 7.868479e-01 5.998341e-01 7.862525e-01 9.552387e-01 9.907984e-01 8.209425e-01
[415] 1.821932e-01 5.186636e-01 9.503833e-01 3.478265e-03 2.604957e-02 7.166133e-01
[421] 6.703210e-01 2.428591e-01 3.469777e-02 4.340735e-01 9.088387e-01 1.095223e-01
[427] 4.201878e-01 9.961317e-01 4.041506e-01 9.614521e-01 3.709122e-02 8.319141e-01
[433] 1.989197e-01 4.927625e-01 6.819089e-01 2.509267e-01 7.273900e-01 2.858779e-01
[439] 1.962540e-01 2.064307e-01 9.503833e-01 3.930212e-01 3.763499e-03 9.003231e-01
[445] 1.268954e-01 8.866787e-01 4.535246e-01 5.898165e-01 1.832637e-01 7.499712e-01
[451] 5.059205e-01 5.793478e-01 5.770433e-01 7.881030e-01 2.573197e-03 1.349357e-01
[457] 8.111609e-01 6.459849e-01 8.203595e-01 6.961009e-01 7.985586e-01 6.239349e-01
[463] 7.875568e-01 1.057744e-01 7.119627e-01 7.712738e-02 3.152409e-01 5.874849e-01
[469] 8.566669e-02 8.791739e-02 7.525360e-01 1.068241e-02 9.947332e-01 5.052059e-01
[475] 6.899238e-03 9.357811e-01 7.094186e-01 4.173879e-01 9.622375e-01 8.124484e-01
[481] 8.239885e-01 9.403178e-01 8.528663e-01 7.671366e-01 2.920779e-02 6.509258e-01
[487] 4.633448e-02 7.013549e-01 1.283787e-01 8.337882e-01 7.469090e-01 8.080637e-01
[493] 3.587932e-01 9.813486e-01 5.814913e-01 1.071320e-02 9.044214e-01 7.268991e-01
[499] 1.917218e-01 2.139352e-02 2.322412e-01 3.175470e-01 9.164145e-01 8.102468e-01
[505] 2.616789e-01 3.201343e-01 8.541053e-01 2.034554e-01 9.307321e-01 2.692509e-01
[511] 8.596940e-01 7.148094e-01 9.874282e-01 9.614521e-01 9.088387e-01 8.456817e-01
[517] 7.484878e-01 8.937505e-01 3.710086e-01 5.547328e-01 1.432235e-01 8.403202e-01
[523] 1.685188e-01 8.286126e-01 6.642235e-01 3.307346e-03 8.890534e-01 1.172922e-02
[529] 8.319141e-01 6.162576e-01 3.556416e-01 6.935260e-01 5.209454e-02 1.548800e-03
[535] 6.988811e-01 7.030830e-01 2.552596e-01 7.060763e-01 4.502030e-01 6.591022e-01
[541] 1.812897e-01 1.654628e-02 6.110401e-01 4.243871e-02 8.302072e-03 9.132375e-01
[547] 1.740296e-03 2.312801e-01 3.640895e-01 3.542636e-01 8.080637e-01 7.181295e-03
[553] 9.698450e-01 8.830380e-01 6.136564e-01 5.924533e-01 3.977917e-01 7.428057e-01
[559] 4.427776e-02 8.883019e-01 8.489791e-01 7.525360e-01 9.942938e-04 1.955315e-01
[565] 7.416584e-01 9.983561e-05 4.532535e-01 5.459525e-02 3.093757e-01 5.491565e-01
[571] 6.847258e-03 2.437698e-01 5.133090e-01 6.073748e-02 4.261591e-01 6.575580e-01
[577] 1.365388e-01 6.313075e-01 9.871003e-02 5.077958e-01 1.798658e-01 5.752641e-01
[583] 5.483586e-01 3.502772e-01 6.819089e-01 8.959440e-02 6.725613e-01 7.632786e-01
[589] 5.150826e-01 4.177051e-01 5.815856e-01 7.211357e-01 6.942038e-01 9.724908e-01
[595] 2.666544e-01 3.412153e-01 1.267720e-01 2.692509e-01 2.794299e-01 6.940853e-01
[601] 9.632105e-01 8.002883e-01 8.534930e-01 1.799761e-01 7.338843e-01 9.049341e-01
[607] 3.282176e-01 5.785217e-01 3.893938e-01 6.703502e-01 8.003122e-01 3.898054e-01
[613] 3.041300e-01 5.350979e-01 5.033614e-01 5.283474e-01 5.059205e-01 7.067294e-01
[619] 6.712224e-01 9.148268e-01 7.176134e-01 6.298124e-01 5.497512e-01 7.149940e-01
[625] 6.508388e-01 9.237689e-01 7.927264e-01 4.283626e-01 2.706089e-03 1.468154e-01
[631] 7.430392e-01 4.073859e-01 3.862157e-01 8.876477e-01 8.217914e-01 8.393452e-01
[637] 4.253596e-01 6.213286e-01 6.058801e-01 1.286684e-01 7.388195e-01 3.277064e-01
[643] 4.015564e-01 4.404877e-01 2.852260e-03 7.296646e-01 4.784470e-01 9.947332e-01
[649] 9.021428e-01 7.833801e-01 1.095659e-02 1.045759e-01 1.581844e-02 8.337882e-01
[655] 7.866538e-01 4.361021e-01 8.459624e-01 3.768246e-01 4.039060e-02 8.218563e-01
[661] 9.255012e-02 5.639625e-01 3.407717e-01 9.610350e-01 3.244106e-01 9.586244e-01
[667] 7.618774e-01 4.111541e-01 3.915806e-01 2.987654e-02 8.961450e-01 1.856044e-01
[673] 7.990455e-01 6.663813e-01 3.705769e-01 9.581099e-01 9.017681e-01 7.827263e-01
[679] 1.005503e-01 3.694953e-01 1.422085e-01 3.572272e-01 8.874925e-01 3.236486e-01
[685] 8.230459e-01 9.079859e-01 7.497605e-01 9.831654e-01 2.700301e-01 1.203657e-01
[691] 3.651540e-01 2.598305e-01 1.299302e-02 2.218764e-01 6.908599e-01 4.105468e-01
[697] 7.773627e-01 8.141099e-01 8.400240e-01 8.297915e-01 4.953654e-02 5.280093e-01
[703] 5.114232e-01 2.969379e-01 6.439512e-04 9.892645e-01 7.968787e-01 3.590568e-02
[709] 1.052802e-03 1.278239e-01 2.944778e-01 1.492121e-01 3.990391e-02 6.381125e-01
[715] 1.811680e-01 9.932315e-01 9.927228e-01 9.807119e-01 6.080262e-03 1.436961e-01
[721] 7.223501e-01 1.377127e-01 8.657105e-01 9.791619e-01 1.913470e-01 6.574340e-01
[727] 4.821980e-01 3.405200e-01 9.529166e-01 7.427605e-01 9.737355e-01 9.217087e-01
[733] 8.414516e-01 7.450421e-01 4.504224e-01 8.393452e-01 9.524954e-01 7.803115e-01
[739] 8.984021e-01 7.854296e-01 7.949017e-01 9.049341e-01 4.507316e-01 8.080637e-01
[745] 7.881030e-01 5.114232e-01 1.407393e-01 6.437864e-01 9.819365e-01 8.112733e-01
[751] 3.260124e-01 9.660842e-01 3.403207e-01 7.174920e-01 6.176959e-01 5.918164e-01
[757] 7.239099e-01 7.236728e-01 8.695939e-01 4.489195e-01 2.905750e-01 5.966984e-01
[763] 9.496276e-01 8.174799e-01 4.988965e-01 9.655597e-01 6.569826e-01 1.425459e-01
[769] 8.529873e-01 4.958292e-01 9.307321e-01 7.428057e-01 9.513471e-01 8.644439e-01
[775] 9.457395e-01 3.324302e-01 7.742214e-03 8.889499e-01 4.920341e-01 5.657651e-01
[781] 1.746258e-01 3.798502e-01 1.471795e-01 9.230991e-01 6.873964e-01 9.874423e-01
[787] 3.565653e-01 7.212800e-01 9.715359e-01 5.465308e-01 6.020425e-01 4.134812e-06
[793] 9.383485e-01 5.620197e-04 9.150933e-01 7.617294e-01 8.370128e-01 5.926201e-01
[799] 5.501601e-01 7.078028e-02 6.622984e-01 8.705515e-01 8.685568e-01 3.942141e-02
[805] 5.423678e-01 1.009407e-01 3.436607e-01 6.333893e-01 5.404068e-02 9.607928e-01
[811] 8.642061e-01 2.766737e-02 9.295109e-01 8.861306e-01 9.689291e-01 8.323555e-01
[817] 6.959960e-01 6.101075e-01 5.460247e-01 4.602242e-01 3.987874e-01 7.369833e-01
[823] 2.293892e-07 6.996231e-02 7.507723e-01 9.420687e-01 9.019169e-01 4.037729e-04
[829] 7.974661e-02 3.509424e-03 9.503833e-01 7.591472e-02 7.258808e-01 7.994108e-01
[835] 8.203595e-01 2.260457e-01 2.938400e-01 2.374467e-01 6.821000e-01 4.220118e-02
[841] 3.082336e-01 9.681767e-01 6.336805e-01 9.875853e-01 5.517748e-01 9.683301e-01
[847] 5.785257e-01 4.431282e-04 3.232449e-01 6.991465e-01 1.797856e-01 9.914955e-01
[853] 5.497512e-01 5.497288e-02 4.471195e-01 2.373853e-01 8.632003e-01 6.712224e-01
[859] 5.272574e-02 7.388195e-01 2.227518e-01 5.752641e-01 8.944730e-01 3.268870e-02
[865] 7.774580e-01 7.499712e-01 9.066061e-01 7.704599e-01 1.594938e-01 4.071737e-01
[871] 3.081408e-01 9.044214e-01 5.648660e-02 2.099723e-01 7.268129e-01 1.797856e-01
[877] 3.152409e-01 9.239815e-01 8.846954e-01 2.475828e-01 8.403202e-01 7.288954e-01
[883] 3.328034e-01 5.590840e-01 5.265708e-01 9.222709e-01 9.530454e-01 6.914790e-01
[889] 3.969626e-02 3.763499e-03 7.794116e-01 9.291177e-01 1.384488e-02 1.375017e-02
[895] 2.353522e-01 8.154762e-01 7.450421e-01 4.538525e-01 6.489444e-01 8.657393e-02
[901] 8.861576e-01 5.847205e-01 5.966984e-01 6.847946e-01 7.508143e-01 7.241870e-01
[907] 2.940924e-02 6.631591e-01 1.997921e-01 1.375290e-01 8.750710e-01 2.005328e-01
[913] 9.112959e-01 9.813460e-01 3.590580e-01 6.517195e-01 6.419391e-02 7.320374e-02
[919] 7.779637e-01 4.466390e-01 5.858361e-01 3.385505e-01 6.588613e-02 9.058614e-01
[925] 9.892645e-01 9.202857e-01 2.874347e-02 6.510972e-01 9.294045e-01 7.044010e-03
[931] 8.808111e-01 6.265972e-01 2.863391e-01 7.986355e-01 6.580503e-01 9.692918e-01
[937] 9.358438e-01 4.212173e-01 8.386441e-01 8.048197e-01 6.454836e-01 7.647182e-01
[943] 3.421066e-01 5.833943e-01 2.562638e-01 4.675727e-01 3.568844e-01 5.349159e-01
[949] 8.438884e-01 6.102118e-01 9.236027e-02 5.209364e-01 7.994260e-02 2.967317e-01
[955] 1.626653e-02 6.752796e-01 6.442712e-01 7.827894e-02 8.719568e-01 5.382182e-01
[961] 8.136030e-01 4.423872e-01 1.534087e-02 9.237689e-01 8.153381e-01 9.398183e-01
[967] 8.861306e-01 3.686137e-01 1.206666e-01 1.790504e-01 2.551328e-01 9.562952e-01
[973] 8.347683e-01 3.764526e-01 7.194784e-01 8.323317e-01 7.424237e-01 5.423766e-01
[979] 7.430392e-01 9.025879e-07 8.522044e-01 8.928593e-01 3.538147e-01 9.074687e-01
[985] 1.532593e-01 8.099872e-01 2.072598e-02 5.712696e-01 6.035685e-01 3.557795e-01
[991] 4.388637e-01 4.262869e-01 3.407717e-01 7.290611e-01 8.459624e-01 8.799227e-01
[997] 1.625577e-02 8.876477e-01 4.361659e-01 6.237420e-01
[ reached getOption("max.print") -- omitted 11897 entries ]
hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values (meta-analysis)")
## Summarize the results. DE are the results from the original analyses of the datasets individually.
# add meta-analysis p-values
H_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
data.table 1.14.2 using 1 threads (see ?getDTthreads). Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********
setDT(H_results_metastats, keep.rownames = "Gene") # make the rownames a part of the dataframe with column name "Gene"
head(H_results_metastats)
# Make columns that give 1 for significant or 0 for not significant
H_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(H_results)
## Dealing with conflicts in the sign of differential expression
signsFC <- mapply(H_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
# this produced only 1155 genes
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
head(unionDE)
[1] 3 7 19 40 46 49
#This keeps only the ones that are significant for either fishcomb or invnormcomb and adds the Signs columns
FC.selecDE <- data.frame(H_results[unionDE,], do.call(cbind,H_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
head(FC.selecDE)
# This is the subset of genes that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
# these are the genes with conflicting signs between HS2008 and HS2012
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(keepDE)
dim(keepDE)
[1] 1055 8
head(conflictDE)
dim(conflictDE)
[1] 100 8
table(FC.selecDE$DE.invnorm)
0 1
128 1027
table(keepDE$DE.invnorm)
0 1
93 962
### Merge results and write to a files
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)
head(H_results_metastats)
# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_Heat08)
[1] 13067 7
dim(top.table_Heat12)
[1] 14431 7
top.table.H_All<- merge(top.table_Heat08,top.table_Heat12,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.H_All)
[1] 14601 13
#merge original results with results from meta-analysis
Heat_results <- merge(top.table.H_All, FC.selecDE, all.x = TRUE)
Heat_results2 <- merge(Heat_results, H_results_metastats, all.x = TRUE)
head(Heat_results2)
write.table(Heat_results2, file = "OutputFiles/Meta_Heat.txt", row.names = F, sep = "\t", quote = F)
Make summary tables
Heat as main effect
## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)
DE IDD Loss IDR IRR
1055.00 265.00 0.00 25.12 0.00
IDD.IRR(invnorm_de, indstudy_de)
DE IDD Loss IDR IRR
962.00 241.00 69.00 25.05 8.73
### Venn Diagram - this was not used; used upset plot instead
#if (require("VennDiagram", quietly = TRUE)) {
#venn.plot<-venn.diagram(x = list(Dataset2008=which(keepDE[,"DE.2008Dataset"]==1), Dataset2012=which(keepDE[,"DE.2012Dataset"]==1),
#invnorm=which(keepDE[,"DE.invnorm"]==1)),
#filename = NULL, col = "black",
#fill = c("blue", "red","green"),
#margin=0.05, alpha = 0.5)
#jpeg("OutputGraphs/venn_Heat.jpg");
#grid.draw(venn.plot);
#dev.off();
#}
Make Upset plot Treatment
https://cran.r-project.org/web/packages/UpSetR/vignettes/basic.usage.html
## upload packages
library(UpSetR)
library(ggplot2)
Warning: package ‘ggplot2’ was built under R version 4.1.2
UpSet Plot
# Heat Treatment 2008
#Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Heat08_top <- top.table_Heat08[which(top.table_Heat08$adj.P.Val<=0.05), ]
dim(UP_Heat08_top)
[1] 75 7
# converting column of "Gene" in dataframe column into vector by passing as index
Treatment_HS2008 = UP_Heat08_top[['Gene']]
# Heat Treatment 2012
#Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Heat12_top <- top.table_Heat12[which(top.table_Heat12$adj.P.Val<=0.05), ]
dim(UP_Heat12_top)
[1] 1101 7
#converting column of "Gene" in dataframe column into vector by passing as index
Treatment_HS2012 = UP_Heat12_top[['Gene']]
# Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- Heat_results2[which(Heat_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)
[1] 962 23
#converting column of "Gene" in dataframe column into vector by passing as index
Treatment_Meta = UP_MetaB[['Gene']]
listHeat <- list(HS2008 = Treatment_HS2008, HS2012 = Treatment_HS2012, MetaAnalysis = Treatment_Meta)
# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_Treatment.pdf")
#make plot
upset(fromList(listHeat), mainbar.y.label = "Number of Differentially Expressed Genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
dev.off()
null device
1
## Main Effect of Ecotype Meta-Analysis
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI01lcmdlIHRvcCB0YWJsZXMgcmVzdWx0cyBmb3IgZWFjaCBkYXRhc2V0IC0ga2VlcCB0aGUgZ2VuZXMgaW4gY29tbW9uIH4xMzAwMFxubGlicmFyeShwbHlyKVxudG9wLnRhYmxlX0VfaW50ZXJzZWN0IDwtIG1lcmdlKHRvcC50YWJsZV9FY28wOCx0b3AudGFibGVfRWNvMTIsYnk9XCJHZW5lXCIsYWxsLnggPSBGQUxTRSwgc3VmZml4ZXMgPSBjKFwiLjA4XCIsIFwiLjEyXCIpKVxuZGltKHRvcC50YWJsZV9FX2ludGVyc2VjdClcbmBgYCJ9 -->
```r
#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_E_intersect <- merge(top.table_Eco08,top.table_Eco12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_E_intersect)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->
```
[1] 12897 13
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtbWFyeSh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->
```r
summary(top.table_E_intersect)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
```
Gene logFC.08 AveExpr.08 t.08 P.Value.08
Length:12897 Min. :-6.5676 Min. :-2.530 Min. :-6.6009 Min. :0.0000059
Class :character 1st Qu.:-0.6835 1st Qu.: 1.378 1st Qu.:-1.1923 1st Qu.:0.1852675
Mode :character Median :-0.1847 Median : 3.343 Median :-0.4656 Median :0.4155463
Mean :-0.3412 Mean : 3.261 Mean :-0.4216 Mean :0.4450561
3rd Qu.: 0.1055 3rd Qu.: 4.970 3rd Qu.: 0.3157 3rd Qu.:0.6941145
Max. : 5.7260 Max. :15.915 Max. : 6.7886 Max. :0.9996759
adj.P.Val.08 B.08 logFC.12 AveExpr.12 t.12
Min. :0.02553 Min. :-6.640 Min. :-7.547935 Min. :-5.122 Min. :-5.68922
1st Qu.:0.74172 1st Qu.:-6.160 1st Qu.:-0.113418 1st Qu.: 1.965 1st Qu.:-0.62686
Median :0.83024 Median :-5.582 Median : 0.009582 Median : 3.609 Median : 0.05543
Mean :0.83149 Mean :-5.433 Mean :-0.002940 Mean : 3.543 Mean : 0.04695
3rd Qu.:0.92665 3rd Qu.:-4.996 3rd Qu.: 0.127366 3rd Qu.: 5.057 3rd Qu.: 0.71856
Max. :0.99968 Max. : 3.789 Max. : 4.356662 Max. :16.730 Max. : 7.18485
P.Value.12 adj.P.Val.12 B.12
Min. :0.0000004 Min. :0.005892 Min. :-6.460
1st Qu.:0.2564461 1st Qu.:0.987533 1st Qu.:-6.249
Median :0.5090662 Median :0.995089 Median :-5.938
Mean :0.5037875 Mean :0.969703 Mean :-5.682
3rd Qu.:0.7547713 3rd Qu.:0.995089 3rd Qu.:-5.425
Max. :0.9999532 Max. :0.999988 Max. : 6.619
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKHRvcC50YWJsZV9FX2ludGVyc2VjdClcbmBgYCJ9 -->
```r
dim(top.table_E_intersect)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->
```
[1] 12897 13
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->
```r
head(top.table_E_intersect)
```
<!-- rnb-source-end -->
<!-- rnb-frame-begin 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 -->
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Gene"],"name":[1],"type":["chr"],"align":["left"]},{"label":["logFC.08"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["AveExpr.08"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["t.08"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["P.Value.08"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.08"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["B.08"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["logFC.12"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["AveExpr.12"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["t.12"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["P.Value.12"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.12"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["B.12"],"name":[13],"type":["dbl"],"align":["right"]}],"data":[{"1":"A1CF","2":"0.77897297","3":"6.907570","4":"4.0510759","5":"0.001029394","6":"0.4483696","7":"-0.8494957","8":"-0.14379954","9":"7.083357","10":"-1.0043599","11":"0.32649209","12":"0.9875332","13":"-5.931452","_rn_":"1"},{"1":"AAAS","2":"-0.41761608","3":"1.997824","4":"-0.8569119","5":"0.404870031","6":"0.8261381","7":"-5.4060601","8":"0.19090169","9":"1.900927","10":"0.9206226","11":"0.36756897","12":"0.9875332","13":"-5.632609","_rn_":"2"},{"1":"AACS","2":"-0.66142363","3":"1.485685","4":"-0.7576295","5":"0.460317473","6":"0.8488405","7":"-5.3835988","8":"-0.05674607","9":"1.825707","10":"-0.2068629","11":"0.83808391","12":"0.9950895","13":"-6.008568","_rn_":"3"},{"1":"AADAT","2":"-0.60902670","3":"4.079697","4":"-1.0293111","5":"0.319517683","6":"0.7947937","7":"-5.7726739","8":"-0.21288352","9":"4.845994","10":"-2.0079454","11":"0.05751435","12":"0.8940819","13":"-4.559747","_rn_":"4"},{"1":"AAGAB","2":"0.23254660","3":"5.983880","4":"0.9259137","5":"0.369033325","6":"0.8099743","7":"-6.1423812","8":"0.07161580","9":"6.234108","10":"0.5885187","11":"0.56238058","12":"0.9950895","13":"-6.278971","_rn_":"5"},{"1":"AAK1","2":"0.05080508","3":"5.952346","4":"0.3262198","5":"0.748729314","6":"0.9409846","7":"-6.5187473","8":"0.05933753","9":"5.027270","10":"0.4568000","11":"0.65244436","12":"0.9950895","13":"-6.341732","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10],"total":[13]},"rows":{"min":[10],"max":[10],"total":[6]},"pages":{}}}
</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jUHV0IHAtdmFsdWUgYW5kIEZvbGQgY2hhbmdlcyByZXN1bHRzIGluIGxpc3RzXG5FX3Jhd3B2YWwgPC0gbGlzdChcInB2YWx1ZV8wOFwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJQLlZhbHVlLjA4XCJdXSwgXCJwdmFsdWVfMTJcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiUC5WYWx1ZS4xMlwiXV0pXG5zdW1tYXJ5KEVfcmF3cHZhbClcbmBgYCJ9 -->
```r
#Put p-value and Fold changes results in lists
E_rawpval <- list("pvalue_08"=top.table_E_intersect[["P.Value.08"]], "pvalue_12"=top.table_E_intersect[["P.Value.12"]])
summary(E_rawpval)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxucHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->
```
Length Class Mode
pvalue_08 12897 -none- numeric
pvalue_12 12897 -none- numeric
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRV9hZGpwdmFsIDwtIGxpc3QoXCJhZGpwdmFsdWVfMDhcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiYWRqLlAuVmFsLjA4XCJdXSwgXCJhZGpwdmFsdWVfMTJcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiYWRqLlAuVmFsLjEyXCJdXSlcbnN1bW1hcnkoRV9hZGpwdmFsKVxuYGBgIn0= -->
```r
E_adjpval <- list("adjpvalue_08"=top.table_E_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_E_intersect[["adj.P.Val.12"]])
summary(E_adjpval)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxuYWRqcHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuYWRqcHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->
```
Length Class Mode
adjpvalue_08 12897 -none- numeric
adjpvalue_12 12897 -none- numeric
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRV9GQyA8LSBsaXN0KFwiRV9GQ18wOFwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJsb2dGQy4wOFwiXV0sIFwiRV9GQ18xMlwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJsb2dGQy4xMlwiXV0pXG5zdW1tYXJ5KEVfRkMpXG5gYGAifQ== -->
```r
E_FC <- list("E_FC_08"=top.table_E_intersect[["logFC.08"]], "E_FC_12"=top.table_E_intersect[["logFC.12"]])
summary(E_FC)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkVfRkNfMDggMTI4OTcgIC1ub25lLSBudW1lcmljXG5FX0ZDXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->
```
Length Class Mode
E_FC_08 12897 -none- numeric
E_FC_12 12897 -none- numeric
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI21ha2UgbWF0cml4IG9mIDEgZm9yIGdlbmVzIERFIGFuZCAwIGZvciBub3QuXG5ERTwtIG1hcHBseShFX2FkanB2YWwsIEZVTj1mdW5jdGlvbih4KSBpZmVsc2UoeCA8PSAwLjA1LCAxLCAwKSlcbnN0dWRpZXMgPC1jKFwiMjAwOERhdGFzZXRcIiwgXCIyMDEyRGF0YXNldFwiKVxuY29sbmFtZXMoREUpPXBhc3RlKFwiREVcIiwgc3R1ZGllcyxzZXA9XCIuXCIpXG5yb3duYW1lcyhERSk9cGFzdGUodG9wLnRhYmxlX0VfaW50ZXJzZWN0JFwiR2VuZVwiKVxuI0RFPC1jYmluZCh0b3AudGFibGVfSF9pbnRlcnNlY3QkXCJHZW5lXCIsIERFKVxuc3VtbWFyeShERSlcbmBgYCJ9 -->
```r
#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(E_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_E_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiIERFLjIwMDhEYXRhc2V0ICAgICAgREUuMjAxMkRhdGFzZXQgICAgIFxuIE1pbi4gICA6MC4wMDAwMDAwICAgTWluLiAgIDowLjAwMDAwMDAgIFxuIDFzdCBRdS46MC4wMDAwMDAwICAgMXN0IFF1LjowLjAwMDAwMDAgIFxuIE1lZGlhbiA6MC4wMDAwMDAwICAgTWVkaWFuIDowLjAwMDAwMDAgIFxuIE1lYW4gICA6MC4wMDA1NDI4ICAgTWVhbiAgIDowLjAwMDMxMDEgIFxuIDNyZCBRdS46MC4wMDAwMDAwICAgM3JkIFF1LjowLjAwMDAwMDAgIFxuIE1heC4gICA6MS4wMDAwMDAwICAgTWF4LiAgIDoxLjAwMDAwMDAgIFxuIn0= -->
```
DE.2008Dataset DE.2012Dataset
Min. :0.0000000 Min. :0.0000000
1st Qu.:0.0000000 1st Qu.:0.0000000
Median :0.0000000 Median :0.0000000
Mean :0.0005428 Mean :0.0003101
3rd Qu.:0.0000000 3rd Qu.:0.0000000
Max. :1.0000000 Max. :1.0000000
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChERSlcbmBgYCJ9 -->
```r
head(DE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgREUuMjAwOERhdGFzZXQgREUuMjAxMkRhdGFzZXRcbkExQ0YgICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUFTICAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFDUyAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbkFBREFUICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUdBQiAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFLMSAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbiJ9 -->
```
DE.2008Dataset DE.2012Dataset
A1CF 0 0
AAAS 0 0
AACS 0 0
AADAT 0 0
AAGAB 0 0
AAK1 0 0
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKERFKVxuYGBgIn0= -->
```r
dim(DE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgICAyXG4ifQ== -->
```
[1] 12897 2
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI0NoZWNrIGZvciBub3JtYWwgZGlzdHJpYnV0aW9uc1xucGFyKG1mcm93ID0gYygxLDIpKVxuaGlzdChFX3Jhd3B2YWxbWzFdXSwgYnJlYWtzPTEwMCwgY29sPVwiZ3JleVwiLCBtYWluPVwiMjAwOCBEYXRhc2V0XCIsIHhsYWI9XCJSYXcgcC12YWx1ZXNcIilcbmhpc3QoRV9yYXdwdmFsW1syXV0sIGJyZWFrcz0xMDAsIGNvbD1cImdyZXlcIiwgbWFpbj1cIjIwMTIgRGF0YXNldFwiLCB4bGFiPVwiUmF3IHAtdmFsdWVzXCIpXG5cbmBgYCJ9 -->
```r
#Check for normal distributions
par(mfrow = c(1,2))
hist(E_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(E_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")
```
<!-- rnb-source-end -->
<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Ecotype: Calculate the meta-analysis p-value
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShtZXRhUk5BU2VxKVxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIEZpc2hlciBtZXRob2QgXG5maXNoY29tYiA8LSBmaXNoZXJjb21iKEVfcmF3cHZhbCwgQkh0aCA9IDAuMDUpXG5zdW1tYXJ5KGZpc2hjb21iKVxuYGBgIn0= -->
```r
library(metaRNASeq)
# P-value combination using Fisher method
fishcomb <- fishercomb(E_rawpval, BHth = 0.05)
summary(fishcomb)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgMjIgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->
```
Length Class Mode
DEindices 22 -none- numeric
TestStatistic 12897 -none- numeric
rawpval 12897 -none- numeric
adjpval 12897 -none- numeric
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChmaXNoY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJGaXNoZXIgTWV0aG9kXCIsIHhsYWIgPSBcIlJhdyBwLXZhbHVlcyBFY290eXBlIChtZXRhLWFuYWx5c2lzKVwiKVxuYGBgIn0= -->
```r
hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values Ecotype (meta-analysis)")
```
<!-- rnb-source-end -->
<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIGludmVyc2Ugbm9tcmFsIG1ldGhvZCBtZXRob2QgXG5pbnZub3JtY29tYiA8LSBpbnZub3JtKEVfcmF3cHZhbCwgbnJlcD1jKDEyLDIwKSwgQkh0aD0wLjA1KVxuc3VtbWFyeShpbnZub3JtY29tYilcbmBgYCJ9 -->
```r
# P-value combination using inverse nomral method method
invnormcomb <- invnorm(E_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgMTQgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->
```
Length Class Mode
DEindices 14 -none- numeric
TestStatistic 12897 -none- numeric
rawpval 12897 -none- numeric
adjpval 12897 -none- numeric
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChpbnZub3JtY29tYilcbmBgYCJ9 -->
```r
head(invnormcomb)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin {"data":"$DEindices\n [1]   583  2874  3217  4459  4773  4862  4863  4864  5020  5457  6129  6601  7119 10197\n\n$TestStatistic\n   [1]  2.242554159  0.414884418 -0.718973343  1.533159512  0.080660917 -0.720439972\n   [7]  0.724254738  0.117591844  0.288926933  2.290752879  0.857763511  2.091247791\n  [13] -1.411130605 -0.111580001  0.359459531 -0.030352195 -1.118782961 -0.506511348\n  [19] -0.437108039  2.729763077 -0.151327480 -1.223857364 -0.089192100 -1.181473516\n  [25]  1.119502736 -0.093984416 -0.109818817  0.367327953  0.329236294 -0.373248755\n  [31]  0.268615437  1.282390929  1.650025705 -0.259429046  1.236211499 -0.338529602\n  [37]  0.078337405  1.344131251  0.465052024  0.372790052  1.249608032  0.947490772\n  [43] -0.590261472  1.375582568 -1.837605869  0.399136492  1.143353033 -0.718879490\n  [49] -0.953160701  0.782820964 -0.059791524 -1.332628428 -1.083695080 -2.030681020\n  [55]  1.187261682 -0.255042812  1.259808547  0.119663902  0.032017051  0.836281313\n  [61] -0.307325919 -0.026308483  0.357949329  1.279757601 -0.651264592  0.617911207\n  [67]  0.206402496  1.380507959  1.676192217  2.054855438 -1.970195349  1.206288629\n  [73]  0.785765950  0.258851909  2.260389582  1.369803650 -0.097740741  0.505229152\n  [79] -1.167835704 -2.165008980  0.484632009 -0.680721750  1.968544126  0.671038858\n  [85] -1.481364084  0.047135166 -0.612932046 -0.229526407  0.123717554  0.430482130\n  [91]  1.255867931  1.381800841  1.030757152  1.481811869  1.058309645 -0.974820767\n  [97] -0.906506394 -0.765523013  0.705273271  1.563093954  0.087373539  0.906116050\n [103] -1.916567554 -0.162183871  1.462247078  1.516086297 -0.139117743 -0.639995625\n [109] -0.011395814 -1.124034066  1.365330759 -2.723028217 -0.495343435 -0.318370495\n [115]  0.354277671  0.751493378 -0.081241362  0.449364891  0.462044310  0.215046477\n [121] -1.398684381 -0.868262644  1.251654556 -0.299492083  0.172769903  1.004348480\n [127]  1.068142447 -0.910976198 -0.439315479 -0.729687472  0.436319054 -0.712100305\n [133]  0.445187842 -0.870359748  0.860383460  0.586524296  0.148227361  1.198857948\n [139]  0.080576201  0.169763896  0.277368387 -1.244888499 -0.025994148  0.839040325\n [145]  0.419496872  0.404768248 -0.620785046  0.051722230  1.953916924  0.074635929\n [151] -1.235561090  0.726612049  0.790330539  0.033499224  0.198737614  1.371093559\n [157] -0.012568716  0.338286377 -0.839368863  1.877879228  1.324590404  1.619155389\n [163] -0.977322006 -0.564647795  2.111761107  1.327932147 -1.376301828 -1.467873256\n [169]  1.417818739  1.224787469  0.905817873  1.878325009 -0.575285438  0.525719090\n [175] -1.245904008 -1.060373127  0.251751324  1.447650308  0.785081006  1.086249078\n [181] -0.869949441  1.381835691  0.806794270  1.474932481 -1.081457327  0.775163884\n [187] -1.275595055 -0.286235782 -0.921797673 -0.814550631  0.689386861  0.373570188\n [193]  0.863016228  2.041896126 -0.062682084  0.133494570 -0.333894045  0.406089383\n [199]  0.993485044  0.689562935 -0.017075593 -1.986604868  0.715822700  0.200115647\n [205]  0.047379995 -0.467083415  1.247781560  0.654072522 -0.667253053  1.177493720\n [211]  0.416715815  1.391884365 -0.753434019  0.426545115  0.095590013  0.403848422\n [217] -0.064617416 -0.775950098 -1.408723219 -0.744115887  0.054403555  1.157840319\n [223]  0.063769852 -1.756248471 -1.328252052  1.516089016 -1.254509334  1.227140847\n [229]  0.291481007  0.215571464  0.239800632  0.112442969  0.498069051  1.090010699\n [235] -1.627111454  0.824555543  0.156868659  0.013153113 -0.010061081 -0.475085604\n [241] -1.541548361  1.956702416  2.019716373  0.289036982  0.488020629  0.643625081\n [247] -0.118051143  0.336202084  1.823900091  0.306273521  0.022922564 -0.368682362\n [253] -1.220734728 -0.461561585  1.743336227  0.118280419 -0.301555654  0.480099477\n [259] -0.823314357 -0.836171339  0.762845079 -0.617897512 -0.590701476 -0.580324708\n [265]  0.308659710 -0.664770186 -0.465091358 -0.276994121  1.308680682  3.196888672\n [271]  0.655222573 -0.861706383 -1.186798759 -0.489903441  1.105575749 -2.268024231\n [277]  0.751346463  0.787931023  0.535846547  1.287481181  0.481541148 -1.378989117\n [283] -1.048131828  0.554258282  0.083759059  0.945840969  0.079231091 -1.998164251\n [289] -1.339224868  0.424329407  0.119650695  0.008843832  0.208410851  0.961643933\n [295] -0.657733848 -1.521429982  0.595719801 -0.394797648  0.909513539 -0.048940741\n [301] -0.831088413  0.299830501 -0.660691074  0.242282028 -0.395704094  1.397404248\n [307]  0.783640044  0.167080907 -0.457257962 -0.694898927 -0.636370676  0.125951135\n [313]  2.120678852  1.602079617  0.261797117  0.828187135  0.959516846 -1.348494051\n [319] -0.353146901 -1.797105327 -0.661123955  0.436968218  1.275677959 -0.328569434\n [325]  1.471815039  0.338653983 -0.577483755 -0.473155792 -0.547054174 -1.746711063\n [331]  0.108262725  1.238781765  1.473506870  0.259719708  1.009867388  1.504908408\n [337] -0.371476369  0.105894704 -0.879297007  0.125106559  1.002712606 -0.699050803\n [343] -0.841449382  1.010298298  0.748937330  3.218918452  0.691375329 -1.106679230\n [349]  1.039397656 -0.774600009  0.386298651  0.436218733 -0.181938674  0.276872262\n [355] -0.634066592 -0.114591627 -0.986559481 -0.913753876 -1.921515951 -0.115111874\n [361]  1.501371008 -0.729773309  0.465398951  2.222503870 -1.319686546 -1.759525052\n [367] -0.115483549  1.152156934 -0.210501374  0.868983971  0.026566455  1.755241633\n [373]  1.419328978 -0.636785604  1.520355505  1.440711808  1.218815951  0.693099624\n [379]  1.893054276 -0.562575378  1.844001826  0.417695221  0.042560090 -1.852455400\n [385]  2.006994678  0.810782970 -0.498405229  0.826630226 -0.639838798 -1.945345801\n [391]  1.270919535 -0.578767636  3.024648204  0.833037889  0.791301286 -1.306187837\n [397] -0.395161066 -0.703401984  0.039532854  1.166132572 -0.726559776 -1.221178900\n [403] -0.409921356 -1.200347507 -1.805127045 -1.266736741  0.131203679 -0.871467076\n [409] -0.280587004  0.644118066 -0.683662244 -1.832191614  1.218966818 -0.289468637\n [415]  0.908379986 -0.364978718  0.395323367  0.703354105 -0.877567479  1.218904623\n [421]  0.297435398  0.388433939 -0.187743836  0.064958650  0.994597390  3.130052398\n [427]  0.538360576  0.305798228  1.768774012 -0.440241447 -0.334071367 -0.477136414\n [433]  0.552993525  1.014764878  0.563000768 -1.118945449 -0.546911831  1.159057132\n [439]  1.427737431  0.797897839 -1.493060415 -1.215586120 -2.047110235  1.545371203\n [445] -0.613640660 -0.618791660  0.987923113  1.250200474 -0.354047334  0.775766487\n [451] -0.386517844  0.405325433 -0.113907316  0.194495258 -0.146224420 -0.523290361\n [457]  0.352647695  1.360539259  1.342375177 -0.322184946  1.141169127  0.584695938\n [463] -0.492224894  0.816955673  0.529678988 -1.273017436 -0.436879328  2.067437254\n [469]  0.397206551  0.137987183 -0.239810064  1.086807567 -1.389999580 -0.563988074\n [475] -0.058526187  0.602661493 -1.086147311  0.395034356  0.387905027  0.444096594\n [481]  0.734757025 -0.020798153  1.384328680 -0.273078506  0.330597313  2.154802669\n [487] -0.323220823  0.815846901 -1.540630111  0.554511833  1.000006883  0.221735773\n [493] -0.704757645  0.266127770 -0.154633523  0.433250205  0.906923960  0.017166998\n [499] -1.256048345 -0.699816640 -0.514064357  0.021734626 -0.220879113  0.893349660\n [505] -0.319147384  0.645359979  0.235882687 -0.551936223  0.297667702  0.211990074\n [511]  2.008174499  1.075330160  0.289110907  0.496367698  0.733620164 -1.039998982\n [517]  1.725060673 -1.566907040 -0.844393882 -1.754865106 -0.825913735 -1.269754470\n [523] -0.626912371  0.295392987 -0.849061679 -0.630726596 -0.017982387 -0.399313316\n [529] -0.202029049 -0.260985266 -0.755375165 -1.074626883  0.605562418  1.366350762\n [535]  0.037791621 -0.476955060  2.097858848 -0.143016967  0.498358820  1.040000433\n [541]  0.632616566 -1.035160550 -0.648445192  1.221192501  0.123864338  1.119007245\n [547] -1.142537034 -1.048766907  0.736490132 -0.118882667  0.409754676  0.323881887\n [553]  1.766428065  0.374058456 -1.846172618  0.824362510 -0.773977903  0.092039395\n [559] -1.242484436  1.513967871  1.487392989  1.210992026 -0.506214603  0.554391162\n [565] -1.029077897 -2.483568408  0.040811298 -1.814523116 -0.217685244  2.109173047\n [571] -0.140047156 -0.158996856  1.500145216  0.703203615  0.961759847 -0.223496873\n [577] -2.227152100  0.010996819  0.044985272 -0.200539204  0.671874725  0.150376483\n [583]  4.217106879  0.405799241 -0.326034277 -0.301334750 -1.570151233 -0.731888139\n [589]  0.448321748  0.288994905  0.170897902 -0.548587138 -0.572266167  1.039318881\n [595]  0.277876028 -0.418097969 -1.340332692  0.008701103  0.374047857 -0.858814125\n [601]  1.250128745  0.463739874  1.312970406 -1.624951230 -0.966967169  0.352602700\n [607] -1.024188759  3.853207731  0.558579582 -0.491657360 -0.804403989 -1.760179000\n [613]  1.084537797  0.217830935  1.209214321 -1.500325967 -1.003723458  1.207633364\n [619]  0.671759043  0.842293475  0.280275410 -1.225022151 -0.647272923 -0.273157403\n [625]  1.928613007  0.650284012  0.484008192  0.894676720  1.172692876  0.155150631\n [631] -1.755305869  0.251756490  0.370921885 -0.254739995  1.068099504  0.993975775\n [637] -1.416573602  0.485805520  0.186645164 -0.574680194 -0.640825444 -2.024833927\n [643] -1.175653017  0.836079598 -0.717746032 -0.163115914  0.109011874 -0.629141519\n [649] -1.143342464  0.126820938  0.152134858 -2.314868038  0.278955172  2.159231600\n [655]  1.533314260  0.141896834 -0.596193420 -0.371543612 -0.970937171 -0.324006916\n [661] -1.705097223  0.578777279  1.524331113  0.843292120  0.744943234 -1.255578607\n [667] -1.104291729 -0.799736256  0.212793147 -1.605994778  0.073043387  1.208535238\n [673] -0.791676765  2.061006875 -0.509795489  0.931069228 -0.855333825 -0.793034118\n [679] -0.454931609 -0.700212170 -1.645494352 -1.151940540 -0.902508695 -0.154977808\n [685] -0.598583325  0.602421964 -0.628070467 -1.184889977 -1.714551766 -1.089723411\n [691] -1.454277342 -1.641245949  0.251481837 -1.801505541  0.028972061 -0.794705764\n [697] -0.212440944  0.911647707 -1.548562095  0.273505823  1.647997963  0.416511411\n [703]  0.638252679 -0.746142494  0.107269986  0.216335778  1.868172549 -1.819409350\n [709] -0.816869310  0.797230196  0.387493461  0.116055552 -0.642143367 -1.279534921\n [715] -0.315254451  0.253560552  0.540545029 -0.032779597  0.655660843 -1.068584816\n [721] -0.764498818  1.313707879  0.401545843  1.222140225  0.127270253 -1.023808177\n [727] -1.562044001  1.165714715  0.140294629  1.055904726  0.564384213  0.492273036\n [733] -0.775109612 -0.173425810 -0.824877657  0.676906268 -0.333519870  0.986986421\n [739]  1.428761698  0.993533684  0.232529216 -0.397318052  0.301345667 -0.846576618\n [745]  0.865149507 -0.644587199  0.456379184 -0.389080614 -1.224738689 -0.365518410\n [751] -0.619178394  1.030021367 -0.141380893  1.126645630 -0.426370098 -0.069528169\n [757] -0.278818291 -1.064839314 -1.059068922 -1.499668756  0.288089042 -0.550107312\n [763]  0.488140185  1.138514318 -0.116719778 -0.101078429  1.307419342 -0.276381845\n [769] -0.758885779 -0.711764505  1.511783748  1.639848662  0.244630013  0.221504219\n [775] -0.654177441  0.565151099 -1.892701249  1.807581066 -0.138592586 -0.384863151\n [781] -1.328224445 -0.545025590  0.313656346 -0.742850997  0.555395690  1.764910715\n [787] -0.625257496  0.966956857  0.483320912  0.047494241  0.923103385 -1.145904961\n [793]  0.280204578 -0.157246698  0.225915308  1.372613433  0.453422888  1.838309733\n [799]  1.787117206 -1.365405640 -0.147755898  1.699103944  0.337587490 -1.611704770\n [805]  0.693109756 -0.123305507 -1.155458265  1.664175618 -0.069740496  0.740024799\n [811] -0.293787881 -1.919715622  0.969118202  0.509963921 -0.063905834 -1.903904615\n [817]  0.435070922 -0.292356360  0.653848260 -1.850615935  1.136108383  0.499201243\n [823] -0.327837152 -0.084480052  0.612819677 -0.042708866  1.130712138 -0.239522824\n [829] -0.158551167 -0.315754423 -0.436354058 -1.198617131  0.404851017 -0.909543566\n [835]  0.353229688 -1.744579803 -0.306018192  0.348498095 -0.152707999 -0.716944496\n [841] -1.388874721  1.560990769 -0.095390740 -0.057209488 -0.252022830  1.859187819\n [847] -0.986649113 -0.229759556 -0.731103239  0.718576904 -0.616917852  0.830422764\n [853]  0.157848180  0.110811531 -0.775401222 -1.049415931  0.767900198  1.592467092\n [859] -1.197716793  0.316866704 -0.637006175  0.371941004  0.242307136 -0.605733062\n [865] -0.636043497  0.211652792  1.317679661  1.294389518 -1.626174875  0.800306956\n [871] -1.645112525  0.374144024 -1.200278539  0.314757542 -0.185958625  0.179286536\n [877]  0.252513167  1.689709834 -1.332819517  0.544371027  0.922871979 -0.738319225\n [883] -2.093646010  0.588330996  1.502005197  0.381838790 -0.749886334  1.003194724\n [889]  0.162243597 -1.281816718 -1.014910074  0.947874399 -0.371075597 -0.491180663\n [895]  1.590762608  1.415442514  0.815223669  1.012712401 -0.813600183 -0.247537835\n [901] -0.169390681  0.141832982 -0.552196642  0.271236362 -0.089839801  1.647228922\n [907]  0.766696029 -0.230373260 -0.106136516 -0.400954538 -0.963893353  0.883750528\n [913]  0.523847897  1.819676877  1.412716175 -1.530344689 -1.421245976  0.227205104\n [919] -0.154543403  2.272050984  1.995647085  0.571895742  0.276329023  0.486612398\n [925] -1.228676897 -1.195814899 -0.296225468 -0.254510825  0.321618835  0.108390809\n [931]  0.424887273 -0.265174589 -1.108739132  1.355058261 -0.153658145  0.241236216\n [937]  1.101775429 -0.164691077  1.508008062  0.865835137  1.209149831  1.092928824\n [943]  1.559715532  0.083439438 -1.567778826 -0.865679833 -1.334725613  0.416579762\n [949]  0.707039404 -0.524122618  0.213682421 -0.016493849 -0.696083746 -0.822524183\n [955] -0.178332394 -1.366533614 -0.515273430 -0.753196884  0.280543968 -0.152612933\n [961]  0.388555929  0.802710215  1.231417192 -0.143886010 -0.514767569  3.179693805\n [967] -0.293815620  0.331125630 -0.903287394  0.761178041  0.217058560  0.480523941\n [973] -0.894898261 -0.944038452  0.027258137  2.095712977  0.929867513 -1.682898374\n [979]  2.206324617 -1.597424804 -1.603449672  0.344756802 -1.011927990  2.134007390\n [985] -0.962194757  0.848933882 -1.798772644  0.863766191  0.567741713 -0.231915472\n [991]  0.220258504 -0.140803669  0.591665065 -1.260877269 -2.204095163 -0.003977565\n [997]  0.949783931  1.263807521  1.154929253  0.624069695\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$rawpval\n   [1] 1.246279e-02 3.391133e-01 7.639213e-01 6.261828e-02 4.678558e-01 7.643729e-01\n   [7] 2.344547e-01 4.531955e-01 3.863186e-01 1.098886e-02 1.955115e-01 1.825293e-02\n  [13] 9.208969e-01 5.444218e-01 3.596257e-01 5.121069e-01 8.683836e-01 6.937511e-01\n  [19] 6.689835e-01 3.168993e-03 5.601413e-01 8.894970e-01 5.355354e-01 8.812927e-01\n  [25] 1.314629e-01 5.374392e-01 5.437235e-01 3.566872e-01 3.709885e-01 6.455183e-01\n  [31] 3.941128e-01 9.985277e-02 4.946884e-02 6.023479e-01 1.081900e-01 6.325179e-01\n  [37] 4.687798e-01 8.945297e-02 3.209471e-01 3.546524e-01 1.057214e-01 1.716944e-01\n  [43] 7.224923e-01 8.447545e-02 9.669397e-01 3.448963e-01 1.264460e-01 7.638924e-01\n  [49] 8.297457e-01 2.168661e-01 5.238392e-01 9.086731e-01 8.607500e-01 9.788563e-01\n  [55] 1.175622e-01 6.006550e-01 1.038692e-01 4.523747e-01 4.872292e-01 2.014983e-01\n  [61] 6.207023e-01 5.104944e-01 3.601906e-01 1.003152e-01 7.425621e-01 2.683169e-01\n  [67] 4.182383e-01 8.371515e-02 4.685027e-02 1.994648e-02 9.755920e-01 1.138531e-01\n  [73] 2.160023e-01 3.978748e-01 1.189854e-02 8.537410e-02 5.389309e-01 3.066989e-01\n  [79] 8.785635e-01 9.848065e-01 3.139687e-01 7.519762e-01 2.450273e-02 2.510979e-01\n  [85] 9.307452e-01 4.812028e-01 7.300394e-01 5.907701e-01 4.507695e-01 3.334225e-01\n  [91] 1.045819e-01 8.351643e-02 1.513274e-01 6.919518e-02 1.449571e-01 8.351754e-01\n  [97] 8.176661e-01 7.780199e-01 2.403201e-01 5.901525e-02 4.651873e-01 1.824372e-01\n [103] 9.723536e-01 5.644195e-01 7.183676e-02 6.474877e-02 5.553214e-01 7.389123e-01\n [109] 5.045462e-01 8.695007e-01 8.607456e-02 9.967657e-01 6.898211e-01 6.248980e-01\n [115] 3.615654e-01 2.261779e-01 5.323750e-01 3.265842e-01 3.220248e-01 4.148655e-01\n [121] 9.190462e-01 8.073747e-01 1.053479e-01 6.177177e-01 4.314161e-01 1.576053e-01\n [127] 1.427281e-01 8.188460e-01 6.697835e-01 7.672094e-01 3.313026e-01 7.617987e-01\n [133] 3.280920e-01 8.079481e-01 1.947889e-01 2.787616e-01 4.410817e-01 1.152916e-01\n [139] 4.678895e-01 4.325979e-01 3.907486e-01 8.934136e-01 5.103690e-01 2.007233e-01\n [145] 3.374265e-01 3.428239e-01 7.326295e-01 4.793750e-01 2.535552e-02 4.702522e-01\n [151] 8.916891e-01 2.337318e-01 2.146674e-01 4.866382e-01 4.212340e-01 8.517290e-02\n [157] 5.050141e-01 3.675737e-01 7.993688e-01 3.019884e-02 9.265352e-02 5.270692e-02\n [163] 8.357951e-01 7.138433e-01 1.735347e-02 9.210026e-02 9.156359e-01 9.289307e-01\n [169] 7.812185e-02 1.103277e-01 1.825161e-01 3.016836e-02 7.174509e-01 2.995417e-01\n [175] 8.936002e-01 8.555126e-01 4.006166e-01 7.385744e-02 2.162030e-01 1.386844e-01\n [181] 8.078360e-01 8.351108e-02 2.098925e-01 7.011535e-02 8.602531e-01 2.191214e-01\n [187] 8.989506e-01 6.126512e-01 8.216829e-01 7.923352e-01 2.452899e-01 3.543621e-01\n [193] 1.940643e-01 2.058092e-02 5.249902e-01 4.469011e-01 6.307702e-01 3.423385e-01\n [199] 1.602368e-01 2.452345e-01 5.068118e-01 9.765169e-01 2.370504e-01 4.206951e-01\n [205] 4.811052e-01 6.797799e-01 1.060555e-01 2.565325e-01 7.476947e-01 1.194993e-01\n [211] 3.384431e-01 8.197871e-02 7.744054e-01 3.348553e-01 4.619231e-01 3.431621e-01\n [217] 5.257607e-01 7.811108e-01 9.205415e-01 7.715968e-01 4.783068e-01 1.234646e-01\n [223] 4.745767e-01 9.604770e-01 9.079526e-01 6.474842e-02 8.951715e-01 1.098848e-01\n [229] 3.853417e-01 4.146609e-01 4.052424e-01 4.552361e-01 3.092177e-01 1.378542e-01\n [235] 9.481433e-01 2.048120e-01 4.376742e-01 4.947528e-01 5.040137e-01 6.826370e-01\n [241] 9.384083e-01 2.519123e-02 2.170641e-02 3.862765e-01 3.127676e-01 2.599093e-01\n [247] 5.469864e-01 3.683592e-01 3.408359e-02 3.796982e-01 4.908560e-01 6.438178e-01\n [253] 8.889068e-01 6.778021e-01 4.063745e-02 4.529227e-01 6.185046e-01 3.155783e-01\n [259] 7.948354e-01 7.984707e-01 2.227779e-01 7.316786e-01 7.226398e-01 7.191522e-01\n [265] 3.787902e-01 7.469013e-01 6.790670e-01 6.091077e-01 9.532127e-02 6.945927e-04\n [271] 2.561622e-01 8.055754e-01 8.823465e-01 6.878989e-01 1.344551e-01 9.883361e-01\n [277] 2.262221e-01 2.153685e-01 2.960323e-01 9.896331e-02 3.150660e-01 9.160509e-01\n [283] 8.527111e-01 2.897010e-01 4.666240e-01 1.721149e-01 4.684244e-01 9.771506e-01\n [289] 9.097513e-01 3.356628e-01 4.523799e-01 4.964719e-01 4.174541e-01 1.681142e-01\n [295] 7.446454e-01 9.359240e-01 2.756812e-01 6.535039e-01 1.815396e-01 5.195167e-01\n [301] 7.970382e-01 3.821532e-01 7.455948e-01 4.042808e-01 6.538383e-01 8.114602e-02\n [307] 2.166257e-01 4.336532e-01 6.762572e-01 7.564407e-01 7.377326e-01 4.498853e-01\n [313] 1.697442e-02 5.456900e-02 3.967389e-01 2.037823e-01 1.686492e-01 9.112502e-01\n [319] 6.380108e-01 9.638405e-01 7.457336e-01 3.310672e-01 1.010347e-01 6.287594e-01\n [325] 7.053542e-02 3.674352e-01 7.181936e-01 6.819490e-01 7.078292e-01 9.596563e-01\n [331] 4.568936e-01 1.077132e-01 7.030721e-02 3.975400e-01 1.562794e-01 6.617381e-02\n [337] 6.448586e-01 4.578329e-01 8.103799e-01 4.502196e-01 1.579998e-01 7.577399e-01\n [343] 7.999519e-01 1.561762e-01 2.269475e-01 6.433754e-04 2.446649e-01 8.657837e-01\n [349] 1.493099e-01 7.807120e-01 3.496377e-01 3.313390e-01 5.721846e-01 3.909391e-01\n [355] 7.369813e-01 5.456156e-01 8.380707e-01 8.195769e-01 9.726667e-01 5.458218e-01\n [361] 6.662981e-02 7.672356e-01 3.208229e-01 1.312464e-02 9.065302e-01 9.607558e-01\n [367] 5.459691e-01 1.246283e-01 5.833618e-01 1.924279e-01 4.894028e-01 3.960899e-02\n [373] 7.790156e-02 7.378677e-01 6.421083e-02 7.483306e-02 1.114570e-01 2.441235e-01\n [379] 2.917533e-02 7.131380e-01 3.259144e-02 3.380850e-01 4.830261e-01 9.680198e-01\n [385] 2.237511e-02 2.087452e-01 6.909008e-01 2.042233e-01 7.388613e-01 9.741333e-01\n [391] 1.018786e-01 7.186270e-01 1.244612e-03 2.024117e-01 2.143841e-01 9.042557e-01\n [397] 6.536380e-01 7.590974e-01 4.842328e-01 1.217804e-01 7.662522e-01 8.889909e-01\n [403] 6.590682e-01 8.849978e-01 9.644726e-01 8.973753e-01 4.478071e-01 8.082504e-01\n [409] 6.104864e-01 2.597494e-01 7.529058e-01 9.665386e-01 1.114284e-01 6.138886e-01\n [415] 1.818387e-01 6.424364e-01 3.463021e-01 2.409176e-01 8.099108e-01 1.114402e-01\n [421] 3.830671e-01 3.488475e-01 5.744613e-01 4.741035e-01 1.599661e-01 8.738756e-04\n [427] 2.951641e-01 3.798791e-01 3.846580e-02 6.701189e-01 6.308371e-01 6.833675e-01\n [433] 2.901339e-01 1.551090e-01 2.867172e-01 8.684183e-01 7.077803e-01 1.232164e-01\n [439] 7.668372e-02 2.124649e-01 9.322893e-01 8.879287e-01 9.796764e-01 6.112825e-02\n [445] 7.302736e-01 7.319732e-01 1.615952e-01 1.056132e-01 6.383483e-01 2.189434e-01\n [451] 6.504434e-01 3.426192e-01 5.453444e-01 4.228941e-01 5.581279e-01 6.996139e-01\n [457] 3.621763e-01 8.682967e-02 8.973719e-02 6.263437e-01 1.268998e-01 2.793761e-01\n [463] 6.887198e-01 2.069769e-01 2.981673e-01 8.984941e-01 6.689006e-01 1.934649e-02\n [469] 3.456076e-01 4.451253e-01 5.947612e-01 1.385609e-01 9.177355e-01 7.136189e-01\n [475] 5.233352e-01 2.733670e-01 8.612931e-01 3.464088e-01 3.490432e-01 3.284864e-01\n [481] 2.312437e-01 5.082967e-01 8.312892e-02 6.076036e-01 3.704743e-01 1.558864e-02\n [487] 6.267360e-01 2.072939e-01 9.382966e-01 2.896143e-01 1.586536e-01 4.122598e-01\n [493] 7.595195e-01 3.950704e-01 5.614449e-01 3.324165e-01 1.822235e-01 4.931517e-01\n [499] 8.954508e-01 7.579791e-01 6.963965e-01 4.913298e-01 5.874067e-01 1.858350e-01\n [505] 6.251926e-01 2.593470e-01 4.067619e-01 7.095040e-01 3.829784e-01 4.160574e-01\n [511] 2.231238e-02 1.411135e-01 3.862483e-01 3.098175e-01 2.315901e-01 8.508298e-01\n [517] 4.225827e-02 9.414318e-01 8.007753e-01 9.603588e-01 7.955735e-01 8.979139e-01\n [523] 7.346417e-01 3.838468e-01 8.020765e-01 7.358903e-01 5.071735e-01 6.551688e-01\n [529] 5.800530e-01 6.029481e-01 7.749880e-01 8.587291e-01 2.724027e-01 8.591445e-02\n [535] 4.849269e-01 6.833029e-01 1.795881e-02 5.568616e-01 3.091156e-01 1.491698e-01\n [541] 2.634920e-01 8.497030e-01 7.416515e-01 1.110066e-01 4.507113e-01 1.315685e-01\n [547] 8.733846e-01 8.528573e-01 2.307162e-01 5.473158e-01 3.409930e-01 3.730137e-01\n [553] 3.866203e-02 3.541804e-01 9.675664e-01 2.048668e-01 7.805281e-01 4.633334e-01\n [559] 8.929711e-01 6.501699e-02 6.845552e-02 1.129492e-01 6.936470e-01 2.896556e-01\n [565] 8.482785e-01 9.934963e-01 4.837232e-01 9.652014e-01 5.861628e-01 1.746482e-02\n [571] 5.556886e-01 5.631643e-01 6.678840e-02 2.409644e-01 1.680851e-01 5.884256e-01\n [577] 9.870314e-01 4.956130e-01 4.820595e-01 5.794705e-01 2.508317e-01 4.402338e-01\n [583] 1.237284e-05 3.424451e-01 6.278008e-01 6.184204e-01 9.418100e-01 7.678816e-01\n [589] 3.269605e-01 3.862926e-01 4.321520e-01 7.083556e-01 7.164292e-01 1.493282e-01\n [595] 3.905538e-01 6.620623e-01 9.099314e-01 4.965288e-01 3.541844e-01 8.047785e-01\n [601] 1.056263e-01 3.214171e-01 9.459646e-02 9.479135e-01 8.332198e-01 3.621932e-01\n [607] 8.471269e-01 5.829020e-05 2.882243e-01 6.885192e-01 7.894182e-01 9.608113e-01\n [613] 1.390632e-01 4.137804e-01 1.132903e-01 9.332350e-01 8.422440e-01 1.135942e-01\n [619] 2.508686e-01 1.998119e-01 3.896331e-01 8.897166e-01 7.412723e-01 6.076339e-01\n [625] 2.688946e-02 2.577544e-01 3.141900e-01 1.854800e-01 1.204595e-01 4.383513e-01\n [631] 9.603965e-01 4.006146e-01 3.553479e-01 6.005380e-01 1.427378e-01 1.601173e-01\n [637] 9.216962e-01 3.135525e-01 4.259694e-01 7.172462e-01 7.391820e-01 9.785578e-01\n [643] 8.801332e-01 2.015551e-01 7.635431e-01 5.647864e-01 4.565965e-01 7.353718e-01\n [649] 8.735518e-01 4.495411e-01 4.395403e-01 9.896899e-01 3.901396e-01 1.541610e-02\n [655] 6.259922e-02 4.435807e-01 7.244770e-01 6.448837e-01 8.342102e-01 6.270336e-01\n [661] 9.559119e-01 2.813697e-01 6.371301e-02 1.995325e-01 2.281530e-01 8.953656e-01\n [667] 8.652667e-01 7.880682e-01 4.157442e-01 9.458625e-01 4.708858e-01 1.134207e-01\n [673] 7.857254e-01 1.965119e-02 6.949026e-01 1.759089e-01 8.038168e-01 7.861210e-01\n [679] 6.754208e-01 7.581026e-01 9.500660e-01 8.753272e-01 8.166066e-01 5.615806e-01\n [685] 7.252746e-01 2.734466e-01 7.350211e-01 8.819695e-01 9.567863e-01 8.620825e-01\n [691] 9.270653e-01 9.496268e-01 4.007208e-01 9.641884e-01 4.884434e-01 7.866077e-01\n [697] 5.841185e-01 1.809771e-01 9.392565e-01 3.922322e-01 4.967654e-02 3.385179e-01\n [703] 2.616546e-01 7.722093e-01 4.572874e-01 4.143630e-01 3.086901e-02 9.655755e-01\n [709] 7.929984e-01 2.126587e-01 3.491955e-01 4.538043e-01 7.396099e-01 8.996456e-01\n [715] 6.237158e-01 3.999175e-01 2.944106e-01 5.130748e-01 2.560212e-01 8.573716e-01\n [721] 7.777150e-01 9.447226e-02 3.440091e-01 1.108273e-01 4.493633e-01 8.470371e-01\n [727] 9.408612e-01 1.218649e-01 4.442136e-01 1.455059e-01 2.862463e-01 3.112632e-01\n [733] 7.808626e-01 5.688416e-01 7.952795e-01 2.492327e-01 6.306291e-01 1.618246e-01\n [739] 7.653637e-02 1.602250e-01 4.080635e-01 6.544335e-01 3.815755e-01 8.013844e-01\n [745] 1.934784e-01 7.404026e-01 3.240587e-01 6.513917e-01 8.896632e-01 6.426378e-01\n [751] 7.321006e-01 1.515000e-01 5.562155e-01 1.299462e-01 6.650809e-01 5.277154e-01\n [757] 6.098079e-01 8.565257e-01 8.552158e-01 9.331499e-01 3.866393e-01 7.088771e-01\n [763] 3.127253e-01 1.274529e-01 5.464589e-01 5.402559e-01 9.553517e-02 6.088726e-01\n [769] 7.760396e-01 7.616947e-01 6.529444e-02 5.051832e-02 4.033715e-01 4.123499e-01\n [775] 7.435013e-01 2.859855e-01 9.708012e-01 3.533586e-02 5.551139e-01 6.498306e-01\n [781] 9.079480e-01 7.071320e-01 3.768910e-01 7.712141e-01 2.893120e-01 3.878939e-02\n [787] 7.340990e-01 1.667828e-01 3.144339e-01 4.810597e-01 1.779767e-01 8.740828e-01\n [793] 3.896603e-01 5.624748e-01 4.106336e-01 8.493628e-02 3.251221e-01 3.300839e-02\n [799] 3.695927e-02 9.139372e-01 5.587323e-01 4.464980e-02 3.678370e-01 9.464869e-01\n [805] 2.441203e-01 5.490674e-01 8.760486e-01 4.803867e-02 5.277999e-01 2.296425e-01\n [811] 6.155400e-01 9.725531e-01 1.662431e-01 3.050384e-01 5.254774e-01 9.715387e-01\n [817] 3.317555e-01 6.149929e-01 2.566048e-01 9.678876e-01 1.279556e-01 3.088188e-01\n [823] 6.284826e-01 5.336626e-01 2.699978e-01 5.170332e-01 1.290881e-01 5.946499e-01\n [829] 5.629887e-01 6.239055e-01 6.687101e-01 8.846616e-01 3.427935e-01 8.184684e-01\n [835] 3.619581e-01 9.594710e-01 6.202046e-01 3.637331e-01 5.606857e-01 7.632958e-01\n [841] 9.175646e-01 5.926296e-02 5.379978e-01 5.228108e-01 5.994883e-01 3.150026e-02\n [847] 8.380927e-01 5.908607e-01 7.676420e-01 2.362008e-01 7.313555e-01 2.031499e-01\n [853] 4.372882e-01 4.558829e-01 7.809487e-01 8.530066e-01 2.212732e-01 5.563990e-02\n [859] 8.844864e-01 3.756724e-01 7.379396e-01 3.549684e-01 4.042711e-01 7.276540e-01\n [865] 7.376260e-01 4.161890e-01 9.380545e-02 9.776545e-02 9.480438e-01 2.117665e-01\n [871] 9.500267e-01 3.541486e-01 8.849844e-01 3.764729e-01 5.737614e-01 4.288564e-01\n [877] 4.003222e-01 4.554174e-02 9.087045e-01 2.930931e-01 1.780370e-01 7.698398e-01\n [883] 9.818542e-01 2.781551e-01 6.654788e-02 3.512905e-01 7.733384e-01 1.578835e-01\n [889] 4.355570e-01 9.000465e-01 8.449257e-01 1.715967e-01 6.447094e-01 6.883507e-01\n [895] 5.583151e-02 7.846940e-02 2.074721e-01 1.555988e-01 7.920630e-01 5.977540e-01\n [901] 5.672553e-01 4.436060e-01 7.095932e-01 3.931046e-01 5.357927e-01 4.975550e-02\n [907] 2.216311e-01 5.910991e-01 5.422630e-01 6.557732e-01 8.324503e-01 1.884154e-01\n [913] 3.001922e-01 3.440411e-02 7.886960e-02 9.370343e-01 9.223774e-01 4.101321e-01\n [919] 5.614094e-01 1.154172e-02 2.298618e-02 2.836963e-01 3.911477e-01 3.132665e-01\n [925] 8.904035e-01 8.841156e-01 6.164710e-01 6.004495e-01 3.738707e-01 4.568428e-01\n [931] 3.354594e-01 6.045625e-01 8.662286e-01 8.769950e-02 5.610604e-01 4.046860e-01\n [937] 1.352797e-01 5.654064e-01 6.577623e-02 1.932903e-01 1.133026e-01 1.372125e-01\n [943] 5.941356e-02 4.667511e-01 9.415336e-01 8.066671e-01 9.090169e-01 3.384929e-01\n [949] 2.397710e-01 6.999034e-01 4.153974e-01 5.065798e-01 7.568118e-01 7.946107e-01\n [955] 5.707690e-01 9.141142e-01 6.968190e-01 7.743342e-01 3.895301e-01 5.606482e-01\n [961] 3.488023e-01 2.110711e-01 1.090834e-01 5.572048e-01 6.966423e-01 7.371537e-04\n [967] 6.155506e-01 3.702748e-01 8.168133e-01 2.232754e-01 4.140814e-01 3.154274e-01\n [973] 8.145793e-01 8.274250e-01 4.891269e-01 1.805383e-02 1.762198e-01 9.538026e-01\n [979] 1.368064e-02 9.449145e-01 9.455823e-01 3.651386e-01 8.442138e-01 1.642109e-02\n [985] 8.320241e-01 1.979590e-01 9.639727e-01 1.938582e-01 2.851052e-01 5.916982e-01\n [991] 4.128349e-01 5.559875e-01 2.770374e-01 8.963235e-01 9.862412e-01 5.015868e-01\n [997] 1.711110e-01 1.031496e-01 1.240597e-01 2.662909e-01\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$adjpval\n   [1] 0.52440419 0.89279215 0.96918126 0.71500137 0.92683058 0.96918126 0.85552134 0.92390705\n   [9] 0.90764482 0.50957206 0.84459318 0.55550242 0.99033160 0.94191862 0.90046873 0.93428026\n  [17] 0.98365938 0.96136134 0.95829409 0.32318679 0.94450938 0.98706540 0.93908978 0.98620664\n  [25] 0.78999636 0.93955873 0.94191862 0.90040511 0.90492793 0.95524669 0.90877004 0.76201255\n  [33] 0.68364643 0.94989500 0.76678599 0.95105638 0.92713456 0.74522893 0.88825145 0.90037895\n  [41] 0.76332487 0.82215393 0.96659618 0.73983348 0.99574836 0.89433057 0.78849278 0.96918126\n  [49] 0.97945173 0.84954658 0.93436330 0.98911652 0.98297565 0.99600225 0.77723210 0.94989500\n  [57] 0.76332487 0.92369922 0.92920930 0.84467934 0.94989500 0.93428026 0.90070653 0.76246919\n  [65] 0.96659618 0.87164469 0.91548181 0.73983348 0.67890785 0.56012964 0.99600225 0.77102540\n  [73] 0.84954658 0.90972921 0.52195744 0.73983348 0.94007266 0.88404605 0.98553143 0.99670795\n  [81] 0.88625333 0.96778464 0.57598811 0.85785024 0.99248735 0.92920930 0.96659618 0.94989500\n  [89] 0.92364987 0.89162706 0.76332487 0.73983348 0.80526947 0.72491290 0.80171548 0.98026460\n  [97] 0.97751568 0.97021732 0.85552134 0.70474043 0.92683058 0.83396455 0.99600225 0.94485384\n [105] 0.72840276 0.71821129 0.94326995 0.96659618 0.93269455 0.98396566 0.73983348 0.99931388\n [113] 0.96136134 0.94997549 0.90084961 0.85307736 0.93772744 0.88965472 0.88825145 0.91419625\n [121] 0.99033160 0.97597710 0.76332487 0.94989500 0.91871704 0.80797901 0.80108338 0.97777321\n [129] 0.95843040 0.96960851 0.89149144 0.96918126 0.89078115 0.97597710 0.84430488 0.87677579\n [137] 0.92120385 0.77252268 0.92683058 0.91871704 0.90841074 0.98725551 0.93428026 0.84467934\n [145] 0.89279215 0.89286469 0.96659618 0.92920930 0.58090325 0.92717524 0.98725551 0.85552134\n [153] 0.84954658 0.92920930 0.91710102 0.73983348 0.93269455 0.90356135 0.97455342 0.61141993\n [161] 0.74573792 0.69222715 0.98026460 0.96466729 0.55550242 0.74573792 0.99033160 0.99217648\n [169] 0.73826078 0.76724823 0.83396455 0.61141993 0.96579612 0.88191763 0.98725551 0.98239800\n [177] 0.91035691 0.73498408 0.84954658 0.79534583 0.97597710 0.73983348 0.84776838 0.72808184\n [185] 0.98297565 0.84954658 0.98836922 0.94989500 0.97815524 0.97225660 0.85662176 0.90037895\n [193] 0.84430488 0.56027293 0.93436330 0.92364987 0.95105638 0.89286469 0.80970596 0.85662176\n [201] 0.93376462 0.99600225 0.85552134 0.91710102 0.92920930 0.96094409 0.76332487 0.86381823\n [209] 0.96709810 0.77906166 0.89279215 0.73983348 0.96990995 0.89241398 0.92550037 0.89294385\n [217] 0.93436330 0.97022562 0.99033160 0.96960851 0.92881111 0.78121570 0.92734270 0.99574428\n [225] 0.98911652 0.71821129 0.98735849 0.76724823 0.90764482 0.91419625 0.91328750 0.92488656\n [233] 0.88518253 0.79534583 0.99550697 0.84690914 0.92006215 0.93139884 0.93269455 0.96136134\n [241] 0.99438435 0.58009205 0.56327473 0.90764482 0.88625333 0.86628174 0.94201918 0.90421186\n [249] 0.61825042 0.90750441 0.92981755 0.95524669 0.98706540 0.96074204 0.65128896 0.92390705\n [257] 0.94989500 0.88625333 0.97319768 0.97455342 0.84954658 0.96659618 0.96659618 0.96603648\n [265] 0.90702881 0.96690825 0.96094409 0.94989500 0.74687631 0.16053347 0.86381823 0.97569808\n [273] 0.98628701 0.96136134 0.79145833 0.99730625 0.85307736 0.84954658 0.87983656 0.75937422\n [281] 0.88625333 0.99033160 0.98239800 0.87802341 0.92683058 0.82366058 0.92713456 0.99600225\n [289] 0.98938039 0.89241398 0.92369922 0.93198595 0.91505811 0.81789021 0.96659618 0.99406023\n [297] 0.87677579 0.95570099 0.83382632 0.93436330 0.97382890 0.90764482 0.96659618 0.91283290\n [305] 0.95570099 0.73983348 0.84954658 0.91932781 0.96053842 0.96918126 0.96659618 0.92364987\n [313] 0.55143345 0.69365196 0.90972921 0.84525754 0.81861836 0.98975865 0.95338764 0.99574428\n [321] 0.96659618 0.89139325 0.76321608 0.95072121 0.72840276 0.90356135 0.96603648 0.96136134\n [329] 0.96427395 0.99574428 0.92511571 0.76575832 0.72831489 0.90972921 0.80779157 0.72034206\n [337] 0.95524669 0.92511571 0.97597710 0.92364987 0.80797901 0.96918126 0.97455342 0.80779157\n [345] 0.85307736 0.15585175 0.85660096 0.98330502 0.80446373 0.97022562 0.89739928 0.89149144\n [353] 0.94665099 0.90841074 0.96659618 0.94201918 0.98040526 0.97790238 0.99600225 0.94201918\n [361] 0.72034206 0.96960851 0.88825145 0.53565961 0.98911652 0.99574428 0.94201918 0.78444662\n [369] 0.94926410 0.84381453 0.92938872 0.64366820 0.73826078 0.96659618 0.71719276 0.73826078\n [377] 0.76724823 0.85647951 0.61083471 0.96466729 0.61801385 0.89279215 0.92920930 0.99574836\n [385] 0.56835783 0.84776838 0.96136134 0.84635875 0.96659618 0.99600225 0.76321608 0.96603648\n [393] 0.20579187 0.84467934 0.84938121 0.98866978 0.95570099 0.96918126 0.92920930 0.78020587\n [401] 0.96933341 0.98706540 0.95654615 0.98655236 0.99574428 0.98808580 0.92364987 0.97597710\n [409] 0.94989500 0.86628174 0.96802170 0.99574428 0.76724823 0.94989500 0.83382632 0.95524669\n [417] 0.89504087 0.85552134 0.97597710 0.76724823 0.90764482 0.89739928 0.94665099 0.92724817\n [425] 0.80970596 0.17339037 0.87936691 0.90750441 0.64321996 0.95843040 0.95105638 0.96136134\n [433] 0.87802341 0.80630401 0.87708773 0.98365938 0.96427395 0.78121570 0.73826078 0.84938121\n [441] 0.99249315 0.98706540 0.99600225 0.71099089 0.96659618 0.96659618 0.80970596 0.76332487\n [449] 0.95338764 0.84954658 0.95531998 0.89286469 0.94191862 0.91714596 0.94350704 0.96212468\n [457] 0.90084961 0.74060640 0.74522893 0.95014213 0.78889384 0.87677579 0.96136134 0.84737522\n [465] 0.88150904 0.98836922 0.95829409 0.56012964 0.89468103 0.92299373 0.94989500 0.79534583\n [473] 0.99033160 0.96466729 0.93436330 0.87665020 0.98297565 0.89504087 0.89739928 0.89078115\n [481] 0.85316301 0.93428026 0.73983348 0.94989500 0.90492793 0.54426323 0.95052684 0.84737522\n [489] 0.99434766 0.87802341 0.80939689 0.91419625 0.96918126 0.90948050 0.94463787 0.89162706\n [497] 0.83396455 0.93090267 0.98748428 0.96918126 0.96136134 0.92998932 0.94960002 0.83684137\n [505] 0.94997549 0.86585499 0.91357642 0.96462062 0.90764482 0.91443288 0.56835783 0.79786953\n [513] 0.90764482 0.88519210 0.85316301 0.98239800 0.66141372 0.99514633 0.97480076 0.99574428\n [521] 0.97319768 0.98812890 0.96659618 0.90764482 0.97480076 0.96659618 0.93383635 0.95584981\n [529] 0.94827850 0.94989500 0.97021732 0.98261282 0.87523104 0.73983348 0.92920930 0.96136134\n [537] 0.55550242 0.94339013 0.88518253 0.80446373 0.86712344 0.98238323 0.96659618 0.76724823\n [545] 0.92364987 0.78999636 0.98472112 0.98239800 0.85316301 0.94201918 0.89286469 0.90492793\n [553] 0.64321996 0.90025929 0.99574836 0.84690914 0.97022562 0.92666296 0.98725551 0.71821129\n [561] 0.72214112 0.76911628 0.96136134 0.87802341 0.98203855 0.99813163 0.92920930 0.99574428\n [569] 0.94960002 0.55550242 0.94326995 0.94472181 0.72034206 0.85552134 0.81789021 0.94980286\n [577] 0.99722621 0.93160571 0.92920930 0.94827850 0.85785024 0.92102828 0.02531949 0.89286469\n [585] 0.95072121 0.94989500 0.99514633 0.96960851 0.88999781 0.90764482 0.91871704 0.96438954\n [593] 0.96531365 0.80446373 0.90841074 0.95745136 0.98949285 0.93198595 0.90025929 0.97569808\n [601] 0.76332487 0.88825145 0.74573792 0.99550697 0.98026460 0.90084961 0.98185659 0.05011791\n [609] 0.87802341 0.96136134 0.97225660 0.99574428 0.79534583 0.91419625 0.77025951 0.99249315\n [617] 0.98124854 0.77037508 0.85785024 0.84467934 0.90841074 0.98706540 0.96659618 0.94989500\n [625] 0.59485595 0.86412672 0.88625333 0.83611852 0.78012432 0.92030222 0.99574428 0.91035691\n [633] 0.90037895 0.94989500 0.80108338 0.80970596 0.99033160 0.88625333 0.91804157 0.96578870\n [641] 0.96659618 0.99600225 0.98553143 0.84467934 0.96918126 0.94485384 0.92511571 0.96659618\n [649] 0.98472112 0.92364987 0.92082994 0.99742369 0.90841074 0.54426323 0.71500137 0.92188710\n [657] 0.96659618 0.95524669 0.98026460 0.95056645 0.99574428 0.87677579 0.71719276 0.84467934\n [665] 0.85307736 0.98747479 0.98330502 0.97209469 0.91422673 0.99535976 0.92724817 0.77025951\n [673] 0.97150277 0.56012964 0.96136134 0.82920205 0.97563506 0.97150277 0.96053842 0.96918126\n [681] 0.99558689 0.98490774 0.97738765 0.94463787 0.96659618 0.87665020 0.96659618 0.98627946\n [689] 0.99574428 0.98297565 0.99206447 0.99550697 0.91035691 0.99574428 0.92926022 0.97164978\n [697] 0.94926410 0.83270127 0.99475257 0.90877004 0.68364643 0.89279215 0.86678945 0.96960851\n [705] 0.92511571 0.91419625 0.61549001 0.99574428 0.97245421 0.84938121 0.89739928 0.92390705\n [713] 0.96659618 0.98836922 0.94989553 0.90997468 0.87934544 0.93428026 0.86381823 0.98261282\n [721] 0.97021732 0.74573792 0.89323253 0.76724823 0.92364987 0.98185659 0.99514633 0.78020587\n [729] 0.92210250 0.80171548 0.87708773 0.88568119 0.97022562 0.94613754 0.97319768 0.85785024\n [737] 0.95105638 0.80970596 0.73826078 0.80970596 0.91383833 0.95570099 0.90764482 0.97480076\n [745] 0.84430488 0.96659618 0.88825145 0.95531998 0.98706540 0.95524669 0.96659618 0.80526947\n [753] 0.94337180 0.78902105 0.95826536 0.93488396 0.94989500 0.98261282 0.98239800 0.99249315\n [761] 0.90764482 0.96462062 0.88625333 0.78889384 0.94201918 0.94083685 0.74719044 0.94989500\n [769] 0.97021732 0.96918126 0.71913102 0.68466885 0.91247498 0.91419625 0.96659618 0.87708773\n [777] 0.99594444 0.62428306 0.94326995 0.95531998 0.98911652 0.96390104 0.90636336 0.96960851\n [785] 0.87802341 0.64321996 0.96659618 0.81686125 0.88625333 0.92920930 0.83073180 0.98478256\n [793] 0.90841074 0.94472181 0.91388186 0.73983348 0.88855692 0.61814328 0.64147782 0.99033160\n [801] 0.94380751 0.67260782 0.90356135 0.99550697 0.85647951 0.94207038 0.98490774 0.68008202\n [809] 0.93488396 0.85316301 0.94989500 0.99600225 0.81615431 0.88290376 0.93436330 0.99600225\n [817] 0.89153087 0.94989500 0.86381823 0.99574836 0.78889384 0.88518253 0.95072121 0.93827387\n [825] 0.87272218 0.93432973 0.78889384 0.94989500 0.94472181 0.94989553 0.95829409 0.98655236\n [833] 0.89286469 0.97777321 0.90084961 0.99574428 0.94989500 0.90195451 0.94450938 0.96918126\n [841] 0.99033160 0.70651734 0.93957585 0.93436330 0.94989500 0.61549001 0.98040526 0.94989500\n [849] 0.96960851 0.85552134 0.96659618 0.84492460 0.92006215 0.92511571 0.97022562 0.98239800\n [857] 0.84954658 0.69804254 0.98655236 0.90521416 0.96659618 0.90037895 0.91283290 0.96659618\n [865] 0.96659618 0.91444679 0.74573792 0.75501860 0.99550697 0.84818397 0.99558689 0.90025929\n [873] 0.98655236 0.90634708 0.94665099 0.91871704 0.91025311 0.67376247 0.98911652 0.87909252\n [881] 0.83073180 0.96960851 0.99618620 0.87677579 0.72034206 0.89867198 0.96990995 0.80797901\n [889] 0.92006215 0.98836922 0.98157879 0.82215393 0.95524669 0.96136134 0.69937073 0.73826078\n [897] 0.84751570 0.80706241 0.97225660 0.94989500 0.94533815 0.92188710 0.96462062 0.90877004\n [905] 0.93908978 0.68364643 0.84954658 0.94989500 0.94191862 0.95627135 0.98019402 0.83975497\n [913] 0.88191763 0.61997274 0.73826078 0.99417822 0.99033160 0.91388186 0.94463787 0.51865333\n [921] 0.57045237 0.87708773 0.90841074 0.88625333 0.98706540 0.98655236 0.94989500 0.94989500\n [929] 0.90492793 0.92511571 0.89241398 0.94989500 0.98330502 0.74222553 0.94463787 0.91322118\n [937] 0.79376785 0.94485384 0.71934764 0.84430488 0.77025951 0.79534583 0.70720158 0.92683058\n [945] 0.99514633 0.97597710 0.98911652 0.89279215 0.85552134 0.96212468 0.91419625 0.93376462\n [953] 0.96918126 0.97304349 0.94665099 0.99033160 0.96136134 0.96990995 0.90841074 0.94450938\n [961] 0.89739928 0.84776838 0.76693423 0.94339013 0.96136134 0.16286312 0.94989500 0.90492793\n [969] 0.97749292 0.85044736 0.91419625 0.88625333 0.97691106 0.97910820 0.92938872 0.55550242\n [977] 0.82975805 0.99574428 0.54426323 0.99514633 0.99528290 0.90250142 0.98157879 0.54426323\n [985] 0.98005434 0.84467934 0.99574428 0.84430488 0.87708773 0.94989500 0.91419625 0.94329711\n [993] 0.87677579 0.98768658 0.99722567 0.93243385 0.82215393 0.76332487 0.78354454 0.86854934\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n"} -->
```
$DEindices
[1] 583 2874 3217 4459 4773 4862 4863 4864 5020 5457 6129 6601 7119 10197
$TestStatistic
[1] 2.242554159 0.414884418 -0.718973343 1.533159512 0.080660917 -0.720439972
[7] 0.724254738 0.117591844 0.288926933 2.290752879 0.857763511 2.091247791
[13] -1.411130605 -0.111580001 0.359459531 -0.030352195 -1.118782961 -0.506511348
[19] -0.437108039 2.729763077 -0.151327480 -1.223857364 -0.089192100 -1.181473516
[25] 1.119502736 -0.093984416 -0.109818817 0.367327953 0.329236294 -0.373248755
[31] 0.268615437 1.282390929 1.650025705 -0.259429046 1.236211499 -0.338529602
[37] 0.078337405 1.344131251 0.465052024 0.372790052 1.249608032 0.947490772
[43] -0.590261472 1.375582568 -1.837605869 0.399136492 1.143353033 -0.718879490
[49] -0.953160701 0.782820964 -0.059791524 -1.332628428 -1.083695080 -2.030681020
[55] 1.187261682 -0.255042812 1.259808547 0.119663902 0.032017051 0.836281313
[61] -0.307325919 -0.026308483 0.357949329 1.279757601 -0.651264592 0.617911207
[67] 0.206402496 1.380507959 1.676192217 2.054855438 -1.970195349 1.206288629
[73] 0.785765950 0.258851909 2.260389582 1.369803650 -0.097740741 0.505229152
[79] -1.167835704 -2.165008980 0.484632009 -0.680721750 1.968544126 0.671038858
[85] -1.481364084 0.047135166 -0.612932046 -0.229526407 0.123717554 0.430482130
[91] 1.255867931 1.381800841 1.030757152 1.481811869 1.058309645 -0.974820767
[97] -0.906506394 -0.765523013 0.705273271 1.563093954 0.087373539 0.906116050
[103] -1.916567554 -0.162183871 1.462247078 1.516086297 -0.139117743 -0.639995625
[109] -0.011395814 -1.124034066 1.365330759 -2.723028217 -0.495343435 -0.318370495
[115] 0.354277671 0.751493378 -0.081241362 0.449364891 0.462044310 0.215046477
[121] -1.398684381 -0.868262644 1.251654556 -0.299492083 0.172769903 1.004348480
[127] 1.068142447 -0.910976198 -0.439315479 -0.729687472 0.436319054 -0.712100305
[133] 0.445187842 -0.870359748 0.860383460 0.586524296 0.148227361 1.198857948
[139] 0.080576201 0.169763896 0.277368387 -1.244888499 -0.025994148 0.839040325
[145] 0.419496872 0.404768248 -0.620785046 0.051722230 1.953916924 0.074635929
[151] -1.235561090 0.726612049 0.790330539 0.033499224 0.198737614 1.371093559
[157] -0.012568716 0.338286377 -0.839368863 1.877879228 1.324590404 1.619155389
[163] -0.977322006 -0.564647795 2.111761107 1.327932147 -1.376301828 -1.467873256
[169] 1.417818739 1.224787469 0.905817873 1.878325009 -0.575285438 0.525719090
[175] -1.245904008 -1.060373127 0.251751324 1.447650308 0.785081006 1.086249078
[181] -0.869949441 1.381835691 0.806794270 1.474932481 -1.081457327 0.775163884
[187] -1.275595055 -0.286235782 -0.921797673 -0.814550631 0.689386861 0.373570188
[193] 0.863016228 2.041896126 -0.062682084 0.133494570 -0.333894045 0.406089383
[199] 0.993485044 0.689562935 -0.017075593 -1.986604868 0.715822700 0.200115647
[205] 0.047379995 -0.467083415 1.247781560 0.654072522 -0.667253053 1.177493720
[211] 0.416715815 1.391884365 -0.753434019 0.426545115 0.095590013 0.403848422
[217] -0.064617416 -0.775950098 -1.408723219 -0.744115887 0.054403555 1.157840319
[223] 0.063769852 -1.756248471 -1.328252052 1.516089016 -1.254509334 1.227140847
[229] 0.291481007 0.215571464 0.239800632 0.112442969 0.498069051 1.090010699
[235] -1.627111454 0.824555543 0.156868659 0.013153113 -0.010061081 -0.475085604
[241] -1.541548361 1.956702416 2.019716373 0.289036982 0.488020629 0.643625081
[247] -0.118051143 0.336202084 1.823900091 0.306273521 0.022922564 -0.368682362
[253] -1.220734728 -0.461561585 1.743336227 0.118280419 -0.301555654 0.480099477
[259] -0.823314357 -0.836171339 0.762845079 -0.617897512 -0.590701476 -0.580324708
[265] 0.308659710 -0.664770186 -0.465091358 -0.276994121 1.308680682 3.196888672
[271] 0.655222573 -0.861706383 -1.186798759 -0.489903441 1.105575749 -2.268024231
[277] 0.751346463 0.787931023 0.535846547 1.287481181 0.481541148 -1.378989117
[283] -1.048131828 0.554258282 0.083759059 0.945840969 0.079231091 -1.998164251
[289] -1.339224868 0.424329407 0.119650695 0.008843832 0.208410851 0.961643933
[295] -0.657733848 -1.521429982 0.595719801 -0.394797648 0.909513539 -0.048940741
[301] -0.831088413 0.299830501 -0.660691074 0.242282028 -0.395704094 1.397404248
[307] 0.783640044 0.167080907 -0.457257962 -0.694898927 -0.636370676 0.125951135
[313] 2.120678852 1.602079617 0.261797117 0.828187135 0.959516846 -1.348494051
[319] -0.353146901 -1.797105327 -0.661123955 0.436968218 1.275677959 -0.328569434
[325] 1.471815039 0.338653983 -0.577483755 -0.473155792 -0.547054174 -1.746711063
[331] 0.108262725 1.238781765 1.473506870 0.259719708 1.009867388 1.504908408
[337] -0.371476369 0.105894704 -0.879297007 0.125106559 1.002712606 -0.699050803
[343] -0.841449382 1.010298298 0.748937330 3.218918452 0.691375329 -1.106679230
[349] 1.039397656 -0.774600009 0.386298651 0.436218733 -0.181938674 0.276872262
[355] -0.634066592 -0.114591627 -0.986559481 -0.913753876 -1.921515951 -0.115111874
[361] 1.501371008 -0.729773309 0.465398951 2.222503870 -1.319686546 -1.759525052
[367] -0.115483549 1.152156934 -0.210501374 0.868983971 0.026566455 1.755241633
[373] 1.419328978 -0.636785604 1.520355505 1.440711808 1.218815951 0.693099624
[379] 1.893054276 -0.562575378 1.844001826 0.417695221 0.042560090 -1.852455400
[385] 2.006994678 0.810782970 -0.498405229 0.826630226 -0.639838798 -1.945345801
[391] 1.270919535 -0.578767636 3.024648204 0.833037889 0.791301286 -1.306187837
[397] -0.395161066 -0.703401984 0.039532854 1.166132572 -0.726559776 -1.221178900
[403] -0.409921356 -1.200347507 -1.805127045 -1.266736741 0.131203679 -0.871467076
[409] -0.280587004 0.644118066 -0.683662244 -1.832191614 1.218966818 -0.289468637
[415] 0.908379986 -0.364978718 0.395323367 0.703354105 -0.877567479 1.218904623
[421] 0.297435398 0.388433939 -0.187743836 0.064958650 0.994597390 3.130052398
[427] 0.538360576 0.305798228 1.768774012 -0.440241447 -0.334071367 -0.477136414
[433] 0.552993525 1.014764878 0.563000768 -1.118945449 -0.546911831 1.159057132
[439] 1.427737431 0.797897839 -1.493060415 -1.215586120 -2.047110235 1.545371203
[445] -0.613640660 -0.618791660 0.987923113 1.250200474 -0.354047334 0.775766487
[451] -0.386517844 0.405325433 -0.113907316 0.194495258 -0.146224420 -0.523290361
[457] 0.352647695 1.360539259 1.342375177 -0.322184946 1.141169127 0.584695938
[463] -0.492224894 0.816955673 0.529678988 -1.273017436 -0.436879328 2.067437254
[469] 0.397206551 0.137987183 -0.239810064 1.086807567 -1.389999580 -0.563988074
[475] -0.058526187 0.602661493 -1.086147311 0.395034356 0.387905027 0.444096594
[481] 0.734757025 -0.020798153 1.384328680 -0.273078506 0.330597313 2.154802669
[487] -0.323220823 0.815846901 -1.540630111 0.554511833 1.000006883 0.221735773
[493] -0.704757645 0.266127770 -0.154633523 0.433250205 0.906923960 0.017166998
[499] -1.256048345 -0.699816640 -0.514064357 0.021734626 -0.220879113 0.893349660
[505] -0.319147384 0.645359979 0.235882687 -0.551936223 0.297667702 0.211990074
[511] 2.008174499 1.075330160 0.289110907 0.496367698 0.733620164 -1.039998982
[517] 1.725060673 -1.566907040 -0.844393882 -1.754865106 -0.825913735 -1.269754470
[523] -0.626912371 0.295392987 -0.849061679 -0.630726596 -0.017982387 -0.399313316
[529] -0.202029049 -0.260985266 -0.755375165 -1.074626883 0.605562418 1.366350762
[535] 0.037791621 -0.476955060 2.097858848 -0.143016967 0.498358820 1.040000433
[541] 0.632616566 -1.035160550 -0.648445192 1.221192501 0.123864338 1.119007245
[547] -1.142537034 -1.048766907 0.736490132 -0.118882667 0.409754676 0.323881887
[553] 1.766428065 0.374058456 -1.846172618 0.824362510 -0.773977903 0.092039395
[559] -1.242484436 1.513967871 1.487392989 1.210992026 -0.506214603 0.554391162
[565] -1.029077897 -2.483568408 0.040811298 -1.814523116 -0.217685244 2.109173047
[571] -0.140047156 -0.158996856 1.500145216 0.703203615 0.961759847 -0.223496873
[577] -2.227152100 0.010996819 0.044985272 -0.200539204 0.671874725 0.150376483
[583] 4.217106879 0.405799241 -0.326034277 -0.301334750 -1.570151233 -0.731888139
[589] 0.448321748 0.288994905 0.170897902 -0.548587138 -0.572266167 1.039318881
[595] 0.277876028 -0.418097969 -1.340332692 0.008701103 0.374047857 -0.858814125
[601] 1.250128745 0.463739874 1.312970406 -1.624951230 -0.966967169 0.352602700
[607] -1.024188759 3.853207731 0.558579582 -0.491657360 -0.804403989 -1.760179000
[613] 1.084537797 0.217830935 1.209214321 -1.500325967 -1.003723458 1.207633364
[619] 0.671759043 0.842293475 0.280275410 -1.225022151 -0.647272923 -0.273157403
[625] 1.928613007 0.650284012 0.484008192 0.894676720 1.172692876 0.155150631
[631] -1.755305869 0.251756490 0.370921885 -0.254739995 1.068099504 0.993975775
[637] -1.416573602 0.485805520 0.186645164 -0.574680194 -0.640825444 -2.024833927
[643] -1.175653017 0.836079598 -0.717746032 -0.163115914 0.109011874 -0.629141519
[649] -1.143342464 0.126820938 0.152134858 -2.314868038 0.278955172 2.159231600
[655] 1.533314260 0.141896834 -0.596193420 -0.371543612 -0.970937171 -0.324006916
[661] -1.705097223 0.578777279 1.524331113 0.843292120 0.744943234 -1.255578607
[667] -1.104291729 -0.799736256 0.212793147 -1.605994778 0.073043387 1.208535238
[673] -0.791676765 2.061006875 -0.509795489 0.931069228 -0.855333825 -0.793034118
[679] -0.454931609 -0.700212170 -1.645494352 -1.151940540 -0.902508695 -0.154977808
[685] -0.598583325 0.602421964 -0.628070467 -1.184889977 -1.714551766 -1.089723411
[691] -1.454277342 -1.641245949 0.251481837 -1.801505541 0.028972061 -0.794705764
[697] -0.212440944 0.911647707 -1.548562095 0.273505823 1.647997963 0.416511411
[703] 0.638252679 -0.746142494 0.107269986 0.216335778 1.868172549 -1.819409350
[709] -0.816869310 0.797230196 0.387493461 0.116055552 -0.642143367 -1.279534921
[715] -0.315254451 0.253560552 0.540545029 -0.032779597 0.655660843 -1.068584816
[721] -0.764498818 1.313707879 0.401545843 1.222140225 0.127270253 -1.023808177
[727] -1.562044001 1.165714715 0.140294629 1.055904726 0.564384213 0.492273036
[733] -0.775109612 -0.173425810 -0.824877657 0.676906268 -0.333519870 0.986986421
[739] 1.428761698 0.993533684 0.232529216 -0.397318052 0.301345667 -0.846576618
[745] 0.865149507 -0.644587199 0.456379184 -0.389080614 -1.224738689 -0.365518410
[751] -0.619178394 1.030021367 -0.141380893 1.126645630 -0.426370098 -0.069528169
[757] -0.278818291 -1.064839314 -1.059068922 -1.499668756 0.288089042 -0.550107312
[763] 0.488140185 1.138514318 -0.116719778 -0.101078429 1.307419342 -0.276381845
[769] -0.758885779 -0.711764505 1.511783748 1.639848662 0.244630013 0.221504219
[775] -0.654177441 0.565151099 -1.892701249 1.807581066 -0.138592586 -0.384863151
[781] -1.328224445 -0.545025590 0.313656346 -0.742850997 0.555395690 1.764910715
[787] -0.625257496 0.966956857 0.483320912 0.047494241 0.923103385 -1.145904961
[793] 0.280204578 -0.157246698 0.225915308 1.372613433 0.453422888 1.838309733
[799] 1.787117206 -1.365405640 -0.147755898 1.699103944 0.337587490 -1.611704770
[805] 0.693109756 -0.123305507 -1.155458265 1.664175618 -0.069740496 0.740024799
[811] -0.293787881 -1.919715622 0.969118202 0.509963921 -0.063905834 -1.903904615
[817] 0.435070922 -0.292356360 0.653848260 -1.850615935 1.136108383 0.499201243
[823] -0.327837152 -0.084480052 0.612819677 -0.042708866 1.130712138 -0.239522824
[829] -0.158551167 -0.315754423 -0.436354058 -1.198617131 0.404851017 -0.909543566
[835] 0.353229688 -1.744579803 -0.306018192 0.348498095 -0.152707999 -0.716944496
[841] -1.388874721 1.560990769 -0.095390740 -0.057209488 -0.252022830 1.859187819
[847] -0.986649113 -0.229759556 -0.731103239 0.718576904 -0.616917852 0.830422764
[853] 0.157848180 0.110811531 -0.775401222 -1.049415931 0.767900198 1.592467092
[859] -1.197716793 0.316866704 -0.637006175 0.371941004 0.242307136 -0.605733062
[865] -0.636043497 0.211652792 1.317679661 1.294389518 -1.626174875 0.800306956
[871] -1.645112525 0.374144024 -1.200278539 0.314757542 -0.185958625 0.179286536
[877] 0.252513167 1.689709834 -1.332819517 0.544371027 0.922871979 -0.738319225
[883] -2.093646010 0.588330996 1.502005197 0.381838790 -0.749886334 1.003194724
[889] 0.162243597 -1.281816718 -1.014910074 0.947874399 -0.371075597 -0.491180663
[895] 1.590762608 1.415442514 0.815223669 1.012712401 -0.813600183 -0.247537835
[901] -0.169390681 0.141832982 -0.552196642 0.271236362 -0.089839801 1.647228922
[907] 0.766696029 -0.230373260 -0.106136516 -0.400954538 -0.963893353 0.883750528
[913] 0.523847897 1.819676877 1.412716175 -1.530344689 -1.421245976 0.227205104
[919] -0.154543403 2.272050984 1.995647085 0.571895742 0.276329023 0.486612398
[925] -1.228676897 -1.195814899 -0.296225468 -0.254510825 0.321618835 0.108390809
[931] 0.424887273 -0.265174589 -1.108739132 1.355058261 -0.153658145 0.241236216
[937] 1.101775429 -0.164691077 1.508008062 0.865835137 1.209149831 1.092928824
[943] 1.559715532 0.083439438 -1.567778826 -0.865679833 -1.334725613 0.416579762
[949] 0.707039404 -0.524122618 0.213682421 -0.016493849 -0.696083746 -0.822524183
[955] -0.178332394 -1.366533614 -0.515273430 -0.753196884 0.280543968 -0.152612933
[961] 0.388555929 0.802710215 1.231417192 -0.143886010 -0.514767569 3.179693805
[967] -0.293815620 0.331125630 -0.903287394 0.761178041 0.217058560 0.480523941
[973] -0.894898261 -0.944038452 0.027258137 2.095712977 0.929867513 -1.682898374
[979] 2.206324617 -1.597424804 -1.603449672 0.344756802 -1.011927990 2.134007390
[985] -0.962194757 0.848933882 -1.798772644 0.863766191 0.567741713 -0.231915472
[991] 0.220258504 -0.140803669 0.591665065 -1.260877269 -2.204095163 -0.003977565
[997] 0.949783931 1.263807521 1.154929253 0.624069695
[ reached getOption("max.print") -- omitted 11897 entries ]
$rawpval
[1] 1.246279e-02 3.391133e-01 7.639213e-01 6.261828e-02 4.678558e-01 7.643729e-01
[7] 2.344547e-01 4.531955e-01 3.863186e-01 1.098886e-02 1.955115e-01 1.825293e-02
[13] 9.208969e-01 5.444218e-01 3.596257e-01 5.121069e-01 8.683836e-01 6.937511e-01
[19] 6.689835e-01 3.168993e-03 5.601413e-01 8.894970e-01 5.355354e-01 8.812927e-01
[25] 1.314629e-01 5.374392e-01 5.437235e-01 3.566872e-01 3.709885e-01 6.455183e-01
[31] 3.941128e-01 9.985277e-02 4.946884e-02 6.023479e-01 1.081900e-01 6.325179e-01
[37] 4.687798e-01 8.945297e-02 3.209471e-01 3.546524e-01 1.057214e-01 1.716944e-01
[43] 7.224923e-01 8.447545e-02 9.669397e-01 3.448963e-01 1.264460e-01 7.638924e-01
[49] 8.297457e-01 2.168661e-01 5.238392e-01 9.086731e-01 8.607500e-01 9.788563e-01
[55] 1.175622e-01 6.006550e-01 1.038692e-01 4.523747e-01 4.872292e-01 2.014983e-01
[61] 6.207023e-01 5.104944e-01 3.601906e-01 1.003152e-01 7.425621e-01 2.683169e-01
[67] 4.182383e-01 8.371515e-02 4.685027e-02 1.994648e-02 9.755920e-01 1.138531e-01
[73] 2.160023e-01 3.978748e-01 1.189854e-02 8.537410e-02 5.389309e-01 3.066989e-01
[79] 8.785635e-01 9.848065e-01 3.139687e-01 7.519762e-01 2.450273e-02 2.510979e-01
[85] 9.307452e-01 4.812028e-01 7.300394e-01 5.907701e-01 4.507695e-01 3.334225e-01
[91] 1.045819e-01 8.351643e-02 1.513274e-01 6.919518e-02 1.449571e-01 8.351754e-01
[97] 8.176661e-01 7.780199e-01 2.403201e-01 5.901525e-02 4.651873e-01 1.824372e-01
[103] 9.723536e-01 5.644195e-01 7.183676e-02 6.474877e-02 5.553214e-01 7.389123e-01
[109] 5.045462e-01 8.695007e-01 8.607456e-02 9.967657e-01 6.898211e-01 6.248980e-01
[115] 3.615654e-01 2.261779e-01 5.323750e-01 3.265842e-01 3.220248e-01 4.148655e-01
[121] 9.190462e-01 8.073747e-01 1.053479e-01 6.177177e-01 4.314161e-01 1.576053e-01
[127] 1.427281e-01 8.188460e-01 6.697835e-01 7.672094e-01 3.313026e-01 7.617987e-01
[133] 3.280920e-01 8.079481e-01 1.947889e-01 2.787616e-01 4.410817e-01 1.152916e-01
[139] 4.678895e-01 4.325979e-01 3.907486e-01 8.934136e-01 5.103690e-01 2.007233e-01
[145] 3.374265e-01 3.428239e-01 7.326295e-01 4.793750e-01 2.535552e-02 4.702522e-01
[151] 8.916891e-01 2.337318e-01 2.146674e-01 4.866382e-01 4.212340e-01 8.517290e-02
[157] 5.050141e-01 3.675737e-01 7.993688e-01 3.019884e-02 9.265352e-02 5.270692e-02
[163] 8.357951e-01 7.138433e-01 1.735347e-02 9.210026e-02 9.156359e-01 9.289307e-01
[169] 7.812185e-02 1.103277e-01 1.825161e-01 3.016836e-02 7.174509e-01 2.995417e-01
[175] 8.936002e-01 8.555126e-01 4.006166e-01 7.385744e-02 2.162030e-01 1.386844e-01
[181] 8.078360e-01 8.351108e-02 2.098925e-01 7.011535e-02 8.602531e-01 2.191214e-01
[187] 8.989506e-01 6.126512e-01 8.216829e-01 7.923352e-01 2.452899e-01 3.543621e-01
[193] 1.940643e-01 2.058092e-02 5.249902e-01 4.469011e-01 6.307702e-01 3.423385e-01
[199] 1.602368e-01 2.452345e-01 5.068118e-01 9.765169e-01 2.370504e-01 4.206951e-01
[205] 4.811052e-01 6.797799e-01 1.060555e-01 2.565325e-01 7.476947e-01 1.194993e-01
[211] 3.384431e-01 8.197871e-02 7.744054e-01 3.348553e-01 4.619231e-01 3.431621e-01
[217] 5.257607e-01 7.811108e-01 9.205415e-01 7.715968e-01 4.783068e-01 1.234646e-01
[223] 4.745767e-01 9.604770e-01 9.079526e-01 6.474842e-02 8.951715e-01 1.098848e-01
[229] 3.853417e-01 4.146609e-01 4.052424e-01 4.552361e-01 3.092177e-01 1.378542e-01
[235] 9.481433e-01 2.048120e-01 4.376742e-01 4.947528e-01 5.040137e-01 6.826370e-01
[241] 9.384083e-01 2.519123e-02 2.170641e-02 3.862765e-01 3.127676e-01 2.599093e-01
[247] 5.469864e-01 3.683592e-01 3.408359e-02 3.796982e-01 4.908560e-01 6.438178e-01
[253] 8.889068e-01 6.778021e-01 4.063745e-02 4.529227e-01 6.185046e-01 3.155783e-01
[259] 7.948354e-01 7.984707e-01 2.227779e-01 7.316786e-01 7.226398e-01 7.191522e-01
[265] 3.787902e-01 7.469013e-01 6.790670e-01 6.091077e-01 9.532127e-02 6.945927e-04
[271] 2.561622e-01 8.055754e-01 8.823465e-01 6.878989e-01 1.344551e-01 9.883361e-01
[277] 2.262221e-01 2.153685e-01 2.960323e-01 9.896331e-02 3.150660e-01 9.160509e-01
[283] 8.527111e-01 2.897010e-01 4.666240e-01 1.721149e-01 4.684244e-01 9.771506e-01
[289] 9.097513e-01 3.356628e-01 4.523799e-01 4.964719e-01 4.174541e-01 1.681142e-01
[295] 7.446454e-01 9.359240e-01 2.756812e-01 6.535039e-01 1.815396e-01 5.195167e-01
[301] 7.970382e-01 3.821532e-01 7.455948e-01 4.042808e-01 6.538383e-01 8.114602e-02
[307] 2.166257e-01 4.336532e-01 6.762572e-01 7.564407e-01 7.377326e-01 4.498853e-01
[313] 1.697442e-02 5.456900e-02 3.967389e-01 2.037823e-01 1.686492e-01 9.112502e-01
[319] 6.380108e-01 9.638405e-01 7.457336e-01 3.310672e-01 1.010347e-01 6.287594e-01
[325] 7.053542e-02 3.674352e-01 7.181936e-01 6.819490e-01 7.078292e-01 9.596563e-01
[331] 4.568936e-01 1.077132e-01 7.030721e-02 3.975400e-01 1.562794e-01 6.617381e-02
[337] 6.448586e-01 4.578329e-01 8.103799e-01 4.502196e-01 1.579998e-01 7.577399e-01
[343] 7.999519e-01 1.561762e-01 2.269475e-01 6.433754e-04 2.446649e-01 8.657837e-01
[349] 1.493099e-01 7.807120e-01 3.496377e-01 3.313390e-01 5.721846e-01 3.909391e-01
[355] 7.369813e-01 5.456156e-01 8.380707e-01 8.195769e-01 9.726667e-01 5.458218e-01
[361] 6.662981e-02 7.672356e-01 3.208229e-01 1.312464e-02 9.065302e-01 9.607558e-01
[367] 5.459691e-01 1.246283e-01 5.833618e-01 1.924279e-01 4.894028e-01 3.960899e-02
[373] 7.790156e-02 7.378677e-01 6.421083e-02 7.483306e-02 1.114570e-01 2.441235e-01
[379] 2.917533e-02 7.131380e-01 3.259144e-02 3.380850e-01 4.830261e-01 9.680198e-01
[385] 2.237511e-02 2.087452e-01 6.909008e-01 2.042233e-01 7.388613e-01 9.741333e-01
[391] 1.018786e-01 7.186270e-01 1.244612e-03 2.024117e-01 2.143841e-01 9.042557e-01
[397] 6.536380e-01 7.590974e-01 4.842328e-01 1.217804e-01 7.662522e-01 8.889909e-01
[403] 6.590682e-01 8.849978e-01 9.644726e-01 8.973753e-01 4.478071e-01 8.082504e-01
[409] 6.104864e-01 2.597494e-01 7.529058e-01 9.665386e-01 1.114284e-01 6.138886e-01
[415] 1.818387e-01 6.424364e-01 3.463021e-01 2.409176e-01 8.099108e-01 1.114402e-01
[421] 3.830671e-01 3.488475e-01 5.744613e-01 4.741035e-01 1.599661e-01 8.738756e-04
[427] 2.951641e-01 3.798791e-01 3.846580e-02 6.701189e-01 6.308371e-01 6.833675e-01
[433] 2.901339e-01 1.551090e-01 2.867172e-01 8.684183e-01 7.077803e-01 1.232164e-01
[439] 7.668372e-02 2.124649e-01 9.322893e-01 8.879287e-01 9.796764e-01 6.112825e-02
[445] 7.302736e-01 7.319732e-01 1.615952e-01 1.056132e-01 6.383483e-01 2.189434e-01
[451] 6.504434e-01 3.426192e-01 5.453444e-01 4.228941e-01 5.581279e-01 6.996139e-01
[457] 3.621763e-01 8.682967e-02 8.973719e-02 6.263437e-01 1.268998e-01 2.793761e-01
[463] 6.887198e-01 2.069769e-01 2.981673e-01 8.984941e-01 6.689006e-01 1.934649e-02
[469] 3.456076e-01 4.451253e-01 5.947612e-01 1.385609e-01 9.177355e-01 7.136189e-01
[475] 5.233352e-01 2.733670e-01 8.612931e-01 3.464088e-01 3.490432e-01 3.284864e-01
[481] 2.312437e-01 5.082967e-01 8.312892e-02 6.076036e-01 3.704743e-01 1.558864e-02
[487] 6.267360e-01 2.072939e-01 9.382966e-01 2.896143e-01 1.586536e-01 4.122598e-01
[493] 7.595195e-01 3.950704e-01 5.614449e-01 3.324165e-01 1.822235e-01 4.931517e-01
[499] 8.954508e-01 7.579791e-01 6.963965e-01 4.913298e-01 5.874067e-01 1.858350e-01
[505] 6.251926e-01 2.593470e-01 4.067619e-01 7.095040e-01 3.829784e-01 4.160574e-01
[511] 2.231238e-02 1.411135e-01 3.862483e-01 3.098175e-01 2.315901e-01 8.508298e-01
[517] 4.225827e-02 9.414318e-01 8.007753e-01 9.603588e-01 7.955735e-01 8.979139e-01
[523] 7.346417e-01 3.838468e-01 8.020765e-01 7.358903e-01 5.071735e-01 6.551688e-01
[529] 5.800530e-01 6.029481e-01 7.749880e-01 8.587291e-01 2.724027e-01 8.591445e-02
[535] 4.849269e-01 6.833029e-01 1.795881e-02 5.568616e-01 3.091156e-01 1.491698e-01
[541] 2.634920e-01 8.497030e-01 7.416515e-01 1.110066e-01 4.507113e-01 1.315685e-01
[547] 8.733846e-01 8.528573e-01 2.307162e-01 5.473158e-01 3.409930e-01 3.730137e-01
[553] 3.866203e-02 3.541804e-01 9.675664e-01 2.048668e-01 7.805281e-01 4.633334e-01
[559] 8.929711e-01 6.501699e-02 6.845552e-02 1.129492e-01 6.936470e-01 2.896556e-01
[565] 8.482785e-01 9.934963e-01 4.837232e-01 9.652014e-01 5.861628e-01 1.746482e-02
[571] 5.556886e-01 5.631643e-01 6.678840e-02 2.409644e-01 1.680851e-01 5.884256e-01
[577] 9.870314e-01 4.956130e-01 4.820595e-01 5.794705e-01 2.508317e-01 4.402338e-01
[583] 1.237284e-05 3.424451e-01 6.278008e-01 6.184204e-01 9.418100e-01 7.678816e-01
[589] 3.269605e-01 3.862926e-01 4.321520e-01 7.083556e-01 7.164292e-01 1.493282e-01
[595] 3.905538e-01 6.620623e-01 9.099314e-01 4.965288e-01 3.541844e-01 8.047785e-01
[601] 1.056263e-01 3.214171e-01 9.459646e-02 9.479135e-01 8.332198e-01 3.621932e-01
[607] 8.471269e-01 5.829020e-05 2.882243e-01 6.885192e-01 7.894182e-01 9.608113e-01
[613] 1.390632e-01 4.137804e-01 1.132903e-01 9.332350e-01 8.422440e-01 1.135942e-01
[619] 2.508686e-01 1.998119e-01 3.896331e-01 8.897166e-01 7.412723e-01 6.076339e-01
[625] 2.688946e-02 2.577544e-01 3.141900e-01 1.854800e-01 1.204595e-01 4.383513e-01
[631] 9.603965e-01 4.006146e-01 3.553479e-01 6.005380e-01 1.427378e-01 1.601173e-01
[637] 9.216962e-01 3.135525e-01 4.259694e-01 7.172462e-01 7.391820e-01 9.785578e-01
[643] 8.801332e-01 2.015551e-01 7.635431e-01 5.647864e-01 4.565965e-01 7.353718e-01
[649] 8.735518e-01 4.495411e-01 4.395403e-01 9.896899e-01 3.901396e-01 1.541610e-02
[655] 6.259922e-02 4.435807e-01 7.244770e-01 6.448837e-01 8.342102e-01 6.270336e-01
[661] 9.559119e-01 2.813697e-01 6.371301e-02 1.995325e-01 2.281530e-01 8.953656e-01
[667] 8.652667e-01 7.880682e-01 4.157442e-01 9.458625e-01 4.708858e-01 1.134207e-01
[673] 7.857254e-01 1.965119e-02 6.949026e-01 1.759089e-01 8.038168e-01 7.861210e-01
[679] 6.754208e-01 7.581026e-01 9.500660e-01 8.753272e-01 8.166066e-01 5.615806e-01
[685] 7.252746e-01 2.734466e-01 7.350211e-01 8.819695e-01 9.567863e-01 8.620825e-01
[691] 9.270653e-01 9.496268e-01 4.007208e-01 9.641884e-01 4.884434e-01 7.866077e-01
[697] 5.841185e-01 1.809771e-01 9.392565e-01 3.922322e-01 4.967654e-02 3.385179e-01
[703] 2.616546e-01 7.722093e-01 4.572874e-01 4.143630e-01 3.086901e-02 9.655755e-01
[709] 7.929984e-01 2.126587e-01 3.491955e-01 4.538043e-01 7.396099e-01 8.996456e-01
[715] 6.237158e-01 3.999175e-01 2.944106e-01 5.130748e-01 2.560212e-01 8.573716e-01
[721] 7.777150e-01 9.447226e-02 3.440091e-01 1.108273e-01 4.493633e-01 8.470371e-01
[727] 9.408612e-01 1.218649e-01 4.442136e-01 1.455059e-01 2.862463e-01 3.112632e-01
[733] 7.808626e-01 5.688416e-01 7.952795e-01 2.492327e-01 6.306291e-01 1.618246e-01
[739] 7.653637e-02 1.602250e-01 4.080635e-01 6.544335e-01 3.815755e-01 8.013844e-01
[745] 1.934784e-01 7.404026e-01 3.240587e-01 6.513917e-01 8.896632e-01 6.426378e-01
[751] 7.321006e-01 1.515000e-01 5.562155e-01 1.299462e-01 6.650809e-01 5.277154e-01
[757] 6.098079e-01 8.565257e-01 8.552158e-01 9.331499e-01 3.866393e-01 7.088771e-01
[763] 3.127253e-01 1.274529e-01 5.464589e-01 5.402559e-01 9.553517e-02 6.088726e-01
[769] 7.760396e-01 7.616947e-01 6.529444e-02 5.051832e-02 4.033715e-01 4.123499e-01
[775] 7.435013e-01 2.859855e-01 9.708012e-01 3.533586e-02 5.551139e-01 6.498306e-01
[781] 9.079480e-01 7.071320e-01 3.768910e-01 7.712141e-01 2.893120e-01 3.878939e-02
[787] 7.340990e-01 1.667828e-01 3.144339e-01 4.810597e-01 1.779767e-01 8.740828e-01
[793] 3.896603e-01 5.624748e-01 4.106336e-01 8.493628e-02 3.251221e-01 3.300839e-02
[799] 3.695927e-02 9.139372e-01 5.587323e-01 4.464980e-02 3.678370e-01 9.464869e-01
[805] 2.441203e-01 5.490674e-01 8.760486e-01 4.803867e-02 5.277999e-01 2.296425e-01
[811] 6.155400e-01 9.725531e-01 1.662431e-01 3.050384e-01 5.254774e-01 9.715387e-01
[817] 3.317555e-01 6.149929e-01 2.566048e-01 9.678876e-01 1.279556e-01 3.088188e-01
[823] 6.284826e-01 5.336626e-01 2.699978e-01 5.170332e-01 1.290881e-01 5.946499e-01
[829] 5.629887e-01 6.239055e-01 6.687101e-01 8.846616e-01 3.427935e-01 8.184684e-01
[835] 3.619581e-01 9.594710e-01 6.202046e-01 3.637331e-01 5.606857e-01 7.632958e-01
[841] 9.175646e-01 5.926296e-02 5.379978e-01 5.228108e-01 5.994883e-01 3.150026e-02
[847] 8.380927e-01 5.908607e-01 7.676420e-01 2.362008e-01 7.313555e-01 2.031499e-01
[853] 4.372882e-01 4.558829e-01 7.809487e-01 8.530066e-01 2.212732e-01 5.563990e-02
[859] 8.844864e-01 3.756724e-01 7.379396e-01 3.549684e-01 4.042711e-01 7.276540e-01
[865] 7.376260e-01 4.161890e-01 9.380545e-02 9.776545e-02 9.480438e-01 2.117665e-01
[871] 9.500267e-01 3.541486e-01 8.849844e-01 3.764729e-01 5.737614e-01 4.288564e-01
[877] 4.003222e-01 4.554174e-02 9.087045e-01 2.930931e-01 1.780370e-01 7.698398e-01
[883] 9.818542e-01 2.781551e-01 6.654788e-02 3.512905e-01 7.733384e-01 1.578835e-01
[889] 4.355570e-01 9.000465e-01 8.449257e-01 1.715967e-01 6.447094e-01 6.883507e-01
[895] 5.583151e-02 7.846940e-02 2.074721e-01 1.555988e-01 7.920630e-01 5.977540e-01
[901] 5.672553e-01 4.436060e-01 7.095932e-01 3.931046e-01 5.357927e-01 4.975550e-02
[907] 2.216311e-01 5.910991e-01 5.422630e-01 6.557732e-01 8.324503e-01 1.884154e-01
[913] 3.001922e-01 3.440411e-02 7.886960e-02 9.370343e-01 9.223774e-01 4.101321e-01
[919] 5.614094e-01 1.154172e-02 2.298618e-02 2.836963e-01 3.911477e-01 3.132665e-01
[925] 8.904035e-01 8.841156e-01 6.164710e-01 6.004495e-01 3.738707e-01 4.568428e-01
[931] 3.354594e-01 6.045625e-01 8.662286e-01 8.769950e-02 5.610604e-01 4.046860e-01
[937] 1.352797e-01 5.654064e-01 6.577623e-02 1.932903e-01 1.133026e-01 1.372125e-01
[943] 5.941356e-02 4.667511e-01 9.415336e-01 8.066671e-01 9.090169e-01 3.384929e-01
[949] 2.397710e-01 6.999034e-01 4.153974e-01 5.065798e-01 7.568118e-01 7.946107e-01
[955] 5.707690e-01 9.141142e-01 6.968190e-01 7.743342e-01 3.895301e-01 5.606482e-01
[961] 3.488023e-01 2.110711e-01 1.090834e-01 5.572048e-01 6.966423e-01 7.371537e-04
[967] 6.155506e-01 3.702748e-01 8.168133e-01 2.232754e-01 4.140814e-01 3.154274e-01
[973] 8.145793e-01 8.274250e-01 4.891269e-01 1.805383e-02 1.762198e-01 9.538026e-01
[979] 1.368064e-02 9.449145e-01 9.455823e-01 3.651386e-01 8.442138e-01 1.642109e-02
[985] 8.320241e-01 1.979590e-01 9.639727e-01 1.938582e-01 2.851052e-01 5.916982e-01
[991] 4.128349e-01 5.559875e-01 2.770374e-01 8.963235e-01 9.862412e-01 5.015868e-01
[997] 1.711110e-01 1.031496e-01 1.240597e-01 2.662909e-01
[ reached getOption("max.print") -- omitted 11897 entries ]
$adjpval
[1] 0.52440419 0.89279215 0.96918126 0.71500137 0.92683058 0.96918126 0.85552134 0.92390705
[9] 0.90764482 0.50957206 0.84459318 0.55550242 0.99033160 0.94191862 0.90046873 0.93428026
[17] 0.98365938 0.96136134 0.95829409 0.32318679 0.94450938 0.98706540 0.93908978 0.98620664
[25] 0.78999636 0.93955873 0.94191862 0.90040511 0.90492793 0.95524669 0.90877004 0.76201255
[33] 0.68364643 0.94989500 0.76678599 0.95105638 0.92713456 0.74522893 0.88825145 0.90037895
[41] 0.76332487 0.82215393 0.96659618 0.73983348 0.99574836 0.89433057 0.78849278 0.96918126
[49] 0.97945173 0.84954658 0.93436330 0.98911652 0.98297565 0.99600225 0.77723210 0.94989500
[57] 0.76332487 0.92369922 0.92920930 0.84467934 0.94989500 0.93428026 0.90070653 0.76246919
[65] 0.96659618 0.87164469 0.91548181 0.73983348 0.67890785 0.56012964 0.99600225 0.77102540
[73] 0.84954658 0.90972921 0.52195744 0.73983348 0.94007266 0.88404605 0.98553143 0.99670795
[81] 0.88625333 0.96778464 0.57598811 0.85785024 0.99248735 0.92920930 0.96659618 0.94989500
[89] 0.92364987 0.89162706 0.76332487 0.73983348 0.80526947 0.72491290 0.80171548 0.98026460
[97] 0.97751568 0.97021732 0.85552134 0.70474043 0.92683058 0.83396455 0.99600225 0.94485384
[105] 0.72840276 0.71821129 0.94326995 0.96659618 0.93269455 0.98396566 0.73983348 0.99931388
[113] 0.96136134 0.94997549 0.90084961 0.85307736 0.93772744 0.88965472 0.88825145 0.91419625
[121] 0.99033160 0.97597710 0.76332487 0.94989500 0.91871704 0.80797901 0.80108338 0.97777321
[129] 0.95843040 0.96960851 0.89149144 0.96918126 0.89078115 0.97597710 0.84430488 0.87677579
[137] 0.92120385 0.77252268 0.92683058 0.91871704 0.90841074 0.98725551 0.93428026 0.84467934
[145] 0.89279215 0.89286469 0.96659618 0.92920930 0.58090325 0.92717524 0.98725551 0.85552134
[153] 0.84954658 0.92920930 0.91710102 0.73983348 0.93269455 0.90356135 0.97455342 0.61141993
[161] 0.74573792 0.69222715 0.98026460 0.96466729 0.55550242 0.74573792 0.99033160 0.99217648
[169] 0.73826078 0.76724823 0.83396455 0.61141993 0.96579612 0.88191763 0.98725551 0.98239800
[177] 0.91035691 0.73498408 0.84954658 0.79534583 0.97597710 0.73983348 0.84776838 0.72808184
[185] 0.98297565 0.84954658 0.98836922 0.94989500 0.97815524 0.97225660 0.85662176 0.90037895
[193] 0.84430488 0.56027293 0.93436330 0.92364987 0.95105638 0.89286469 0.80970596 0.85662176
[201] 0.93376462 0.99600225 0.85552134 0.91710102 0.92920930 0.96094409 0.76332487 0.86381823
[209] 0.96709810 0.77906166 0.89279215 0.73983348 0.96990995 0.89241398 0.92550037 0.89294385
[217] 0.93436330 0.97022562 0.99033160 0.96960851 0.92881111 0.78121570 0.92734270 0.99574428
[225] 0.98911652 0.71821129 0.98735849 0.76724823 0.90764482 0.91419625 0.91328750 0.92488656
[233] 0.88518253 0.79534583 0.99550697 0.84690914 0.92006215 0.93139884 0.93269455 0.96136134
[241] 0.99438435 0.58009205 0.56327473 0.90764482 0.88625333 0.86628174 0.94201918 0.90421186
[249] 0.61825042 0.90750441 0.92981755 0.95524669 0.98706540 0.96074204 0.65128896 0.92390705
[257] 0.94989500 0.88625333 0.97319768 0.97455342 0.84954658 0.96659618 0.96659618 0.96603648
[265] 0.90702881 0.96690825 0.96094409 0.94989500 0.74687631 0.16053347 0.86381823 0.97569808
[273] 0.98628701 0.96136134 0.79145833 0.99730625 0.85307736 0.84954658 0.87983656 0.75937422
[281] 0.88625333 0.99033160 0.98239800 0.87802341 0.92683058 0.82366058 0.92713456 0.99600225
[289] 0.98938039 0.89241398 0.92369922 0.93198595 0.91505811 0.81789021 0.96659618 0.99406023
[297] 0.87677579 0.95570099 0.83382632 0.93436330 0.97382890 0.90764482 0.96659618 0.91283290
[305] 0.95570099 0.73983348 0.84954658 0.91932781 0.96053842 0.96918126 0.96659618 0.92364987
[313] 0.55143345 0.69365196 0.90972921 0.84525754 0.81861836 0.98975865 0.95338764 0.99574428
[321] 0.96659618 0.89139325 0.76321608 0.95072121 0.72840276 0.90356135 0.96603648 0.96136134
[329] 0.96427395 0.99574428 0.92511571 0.76575832 0.72831489 0.90972921 0.80779157 0.72034206
[337] 0.95524669 0.92511571 0.97597710 0.92364987 0.80797901 0.96918126 0.97455342 0.80779157
[345] 0.85307736 0.15585175 0.85660096 0.98330502 0.80446373 0.97022562 0.89739928 0.89149144
[353] 0.94665099 0.90841074 0.96659618 0.94201918 0.98040526 0.97790238 0.99600225 0.94201918
[361] 0.72034206 0.96960851 0.88825145 0.53565961 0.98911652 0.99574428 0.94201918 0.78444662
[369] 0.94926410 0.84381453 0.92938872 0.64366820 0.73826078 0.96659618 0.71719276 0.73826078
[377] 0.76724823 0.85647951 0.61083471 0.96466729 0.61801385 0.89279215 0.92920930 0.99574836
[385] 0.56835783 0.84776838 0.96136134 0.84635875 0.96659618 0.99600225 0.76321608 0.96603648
[393] 0.20579187 0.84467934 0.84938121 0.98866978 0.95570099 0.96918126 0.92920930 0.78020587
[401] 0.96933341 0.98706540 0.95654615 0.98655236 0.99574428 0.98808580 0.92364987 0.97597710
[409] 0.94989500 0.86628174 0.96802170 0.99574428 0.76724823 0.94989500 0.83382632 0.95524669
[417] 0.89504087 0.85552134 0.97597710 0.76724823 0.90764482 0.89739928 0.94665099 0.92724817
[425] 0.80970596 0.17339037 0.87936691 0.90750441 0.64321996 0.95843040 0.95105638 0.96136134
[433] 0.87802341 0.80630401 0.87708773 0.98365938 0.96427395 0.78121570 0.73826078 0.84938121
[441] 0.99249315 0.98706540 0.99600225 0.71099089 0.96659618 0.96659618 0.80970596 0.76332487
[449] 0.95338764 0.84954658 0.95531998 0.89286469 0.94191862 0.91714596 0.94350704 0.96212468
[457] 0.90084961 0.74060640 0.74522893 0.95014213 0.78889384 0.87677579 0.96136134 0.84737522
[465] 0.88150904 0.98836922 0.95829409 0.56012964 0.89468103 0.92299373 0.94989500 0.79534583
[473] 0.99033160 0.96466729 0.93436330 0.87665020 0.98297565 0.89504087 0.89739928 0.89078115
[481] 0.85316301 0.93428026 0.73983348 0.94989500 0.90492793 0.54426323 0.95052684 0.84737522
[489] 0.99434766 0.87802341 0.80939689 0.91419625 0.96918126 0.90948050 0.94463787 0.89162706
[497] 0.83396455 0.93090267 0.98748428 0.96918126 0.96136134 0.92998932 0.94960002 0.83684137
[505] 0.94997549 0.86585499 0.91357642 0.96462062 0.90764482 0.91443288 0.56835783 0.79786953
[513] 0.90764482 0.88519210 0.85316301 0.98239800 0.66141372 0.99514633 0.97480076 0.99574428
[521] 0.97319768 0.98812890 0.96659618 0.90764482 0.97480076 0.96659618 0.93383635 0.95584981
[529] 0.94827850 0.94989500 0.97021732 0.98261282 0.87523104 0.73983348 0.92920930 0.96136134
[537] 0.55550242 0.94339013 0.88518253 0.80446373 0.86712344 0.98238323 0.96659618 0.76724823
[545] 0.92364987 0.78999636 0.98472112 0.98239800 0.85316301 0.94201918 0.89286469 0.90492793
[553] 0.64321996 0.90025929 0.99574836 0.84690914 0.97022562 0.92666296 0.98725551 0.71821129
[561] 0.72214112 0.76911628 0.96136134 0.87802341 0.98203855 0.99813163 0.92920930 0.99574428
[569] 0.94960002 0.55550242 0.94326995 0.94472181 0.72034206 0.85552134 0.81789021 0.94980286
[577] 0.99722621 0.93160571 0.92920930 0.94827850 0.85785024 0.92102828 0.02531949 0.89286469
[585] 0.95072121 0.94989500 0.99514633 0.96960851 0.88999781 0.90764482 0.91871704 0.96438954
[593] 0.96531365 0.80446373 0.90841074 0.95745136 0.98949285 0.93198595 0.90025929 0.97569808
[601] 0.76332487 0.88825145 0.74573792 0.99550697 0.98026460 0.90084961 0.98185659 0.05011791
[609] 0.87802341 0.96136134 0.97225660 0.99574428 0.79534583 0.91419625 0.77025951 0.99249315
[617] 0.98124854 0.77037508 0.85785024 0.84467934 0.90841074 0.98706540 0.96659618 0.94989500
[625] 0.59485595 0.86412672 0.88625333 0.83611852 0.78012432 0.92030222 0.99574428 0.91035691
[633] 0.90037895 0.94989500 0.80108338 0.80970596 0.99033160 0.88625333 0.91804157 0.96578870
[641] 0.96659618 0.99600225 0.98553143 0.84467934 0.96918126 0.94485384 0.92511571 0.96659618
[649] 0.98472112 0.92364987 0.92082994 0.99742369 0.90841074 0.54426323 0.71500137 0.92188710
[657] 0.96659618 0.95524669 0.98026460 0.95056645 0.99574428 0.87677579 0.71719276 0.84467934
[665] 0.85307736 0.98747479 0.98330502 0.97209469 0.91422673 0.99535976 0.92724817 0.77025951
[673] 0.97150277 0.56012964 0.96136134 0.82920205 0.97563506 0.97150277 0.96053842 0.96918126
[681] 0.99558689 0.98490774 0.97738765 0.94463787 0.96659618 0.87665020 0.96659618 0.98627946
[689] 0.99574428 0.98297565 0.99206447 0.99550697 0.91035691 0.99574428 0.92926022 0.97164978
[697] 0.94926410 0.83270127 0.99475257 0.90877004 0.68364643 0.89279215 0.86678945 0.96960851
[705] 0.92511571 0.91419625 0.61549001 0.99574428 0.97245421 0.84938121 0.89739928 0.92390705
[713] 0.96659618 0.98836922 0.94989553 0.90997468 0.87934544 0.93428026 0.86381823 0.98261282
[721] 0.97021732 0.74573792 0.89323253 0.76724823 0.92364987 0.98185659 0.99514633 0.78020587
[729] 0.92210250 0.80171548 0.87708773 0.88568119 0.97022562 0.94613754 0.97319768 0.85785024
[737] 0.95105638 0.80970596 0.73826078 0.80970596 0.91383833 0.95570099 0.90764482 0.97480076
[745] 0.84430488 0.96659618 0.88825145 0.95531998 0.98706540 0.95524669 0.96659618 0.80526947
[753] 0.94337180 0.78902105 0.95826536 0.93488396 0.94989500 0.98261282 0.98239800 0.99249315
[761] 0.90764482 0.96462062 0.88625333 0.78889384 0.94201918 0.94083685 0.74719044 0.94989500
[769] 0.97021732 0.96918126 0.71913102 0.68466885 0.91247498 0.91419625 0.96659618 0.87708773
[777] 0.99594444 0.62428306 0.94326995 0.95531998 0.98911652 0.96390104 0.90636336 0.96960851
[785] 0.87802341 0.64321996 0.96659618 0.81686125 0.88625333 0.92920930 0.83073180 0.98478256
[793] 0.90841074 0.94472181 0.91388186 0.73983348 0.88855692 0.61814328 0.64147782 0.99033160
[801] 0.94380751 0.67260782 0.90356135 0.99550697 0.85647951 0.94207038 0.98490774 0.68008202
[809] 0.93488396 0.85316301 0.94989500 0.99600225 0.81615431 0.88290376 0.93436330 0.99600225
[817] 0.89153087 0.94989500 0.86381823 0.99574836 0.78889384 0.88518253 0.95072121 0.93827387
[825] 0.87272218 0.93432973 0.78889384 0.94989500 0.94472181 0.94989553 0.95829409 0.98655236
[833] 0.89286469 0.97777321 0.90084961 0.99574428 0.94989500 0.90195451 0.94450938 0.96918126
[841] 0.99033160 0.70651734 0.93957585 0.93436330 0.94989500 0.61549001 0.98040526 0.94989500
[849] 0.96960851 0.85552134 0.96659618 0.84492460 0.92006215 0.92511571 0.97022562 0.98239800
[857] 0.84954658 0.69804254 0.98655236 0.90521416 0.96659618 0.90037895 0.91283290 0.96659618
[865] 0.96659618 0.91444679 0.74573792 0.75501860 0.99550697 0.84818397 0.99558689 0.90025929
[873] 0.98655236 0.90634708 0.94665099 0.91871704 0.91025311 0.67376247 0.98911652 0.87909252
[881] 0.83073180 0.96960851 0.99618620 0.87677579 0.72034206 0.89867198 0.96990995 0.80797901
[889] 0.92006215 0.98836922 0.98157879 0.82215393 0.95524669 0.96136134 0.69937073 0.73826078
[897] 0.84751570 0.80706241 0.97225660 0.94989500 0.94533815 0.92188710 0.96462062 0.90877004
[905] 0.93908978 0.68364643 0.84954658 0.94989500 0.94191862 0.95627135 0.98019402 0.83975497
[913] 0.88191763 0.61997274 0.73826078 0.99417822 0.99033160 0.91388186 0.94463787 0.51865333
[921] 0.57045237 0.87708773 0.90841074 0.88625333 0.98706540 0.98655236 0.94989500 0.94989500
[929] 0.90492793 0.92511571 0.89241398 0.94989500 0.98330502 0.74222553 0.94463787 0.91322118
[937] 0.79376785 0.94485384 0.71934764 0.84430488 0.77025951 0.79534583 0.70720158 0.92683058
[945] 0.99514633 0.97597710 0.98911652 0.89279215 0.85552134 0.96212468 0.91419625 0.93376462
[953] 0.96918126 0.97304349 0.94665099 0.99033160 0.96136134 0.96990995 0.90841074 0.94450938
[961] 0.89739928 0.84776838 0.76693423 0.94339013 0.96136134 0.16286312 0.94989500 0.90492793
[969] 0.97749292 0.85044736 0.91419625 0.88625333 0.97691106 0.97910820 0.92938872 0.55550242
[977] 0.82975805 0.99574428 0.54426323 0.99514633 0.99528290 0.90250142 0.98157879 0.54426323
[985] 0.98005434 0.84467934 0.99574428 0.84430488 0.87708773 0.94989500 0.91419625 0.94329711
[993] 0.87677579 0.98768658 0.99722567 0.93243385 0.82215393 0.76332487 0.78354454 0.86854934
[ reached getOption("max.print") -- omitted 11897 entries ]
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChpbnZub3JtY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJJbnZlcnNlIG5vcm1hbCBtZXRob2RcIiwgeGxhYiA9IFwiUmF3IHAtdmFsdWVzIEVjb3R5cGUgKG1ldGEtYW5hbHlzaXMpXCIpXG5gYGAifQ== -->
```r
hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values Ecotype (meta-analysis)")
```
<!-- rnb-source-end -->
<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->
<img src="data:image/png;base64,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```r
## Summarize the results. DE are the results from the original anlayses of the datasets individually.
E_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(E_results_metastats, keep.rownames = "Gene") # make the rownames a part of the dataframe with column name "Gene"
head(E_results_metastats)
```
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```r
E_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(E_results)
```
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</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin 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 -->
```r
## Dealing with conflicts in the sign of differential expression
signsFC <- mapply(E_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(E_results[unionDE,], do.call(cbind,E_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
dim(FC.selecDE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIyICA4XG4ifQ== -->
```
[1] 22 8
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuICAjIGtlZXAgdGhlIG9uZXMgdGhhdCBkb24ndCBoYXZlIGEgY29uZmxpY3QgKDEgb3IgLTEpXG5rZWVwREUgPC0gRkMuc2VsZWNERVt3aGljaChhYnMoRkMuc2VsZWNERSRzaWduRkMpPT0xKSxdXG5kaW0oa2VlcERFKVxuYGBgIn0= -->
```r
# keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
dim(keepDE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE4ICA4XG4ifQ== -->
```
[1] 18 8
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY29uZmxpY3RERSA8LSBGQy5zZWxlY0RFW3doaWNoKEZDLnNlbGVjREUkc2lnbkZDPT0wKSxdXG5kaW0oY29uZmxpY3RERSlcbmBgYCJ9 -->
```r
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
dim(conflictDE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQgOFxuIn0= -->
```
[1] 4 8
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChGQy5zZWxlY0RFKSAgIyB0aGUgb25lcyB3aXRoIGNvbmZsaWN0ID0gMFxuYGBgIn0= -->
```r
head(FC.selecDE) # the ones with conflict = 0
```
<!-- rnb-source-end -->
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</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChrZWVwREUpXG5gYGAifQ== -->
```r
head(keepDE)
```
<!-- rnb-source-end -->
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</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChjb25mbGljdERFKVxuYGBgIn0= -->
```r
head(conflictDE)
```
<!-- rnb-source-end -->
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<div data-pagedtable="false">
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</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudGFibGUoY29uZmxpY3RERSRERSlcbmBgYCJ9 -->
```r
table(conflictDE$DE)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG4wIFxuNCBcbiJ9 -->
```
0
4
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMjIE1lcmdlIHJlc3VsdHMgYW5kIHdyaXRlIHRvICBhIGZpbGVcbnNldERUKEZDLnNlbGVjREUsIGtlZXAucm93bmFtZXMgPSBcIkdlbmVcIilcbmhlYWQoRkMuc2VsZWNERSlcbmBgYCJ9 -->
```r
### Merge results and write to a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)
```
<!-- rnb-source-end -->
<!-- rnb-frame-begin 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 -->
<div data-pagedtable="false">
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</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->
```r
head(top.table_E_intersect)
```
<!-- rnb-source-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChFX3Jlc3VsdHNfbWV0YXN0YXRzKVxuYGBgIn0= -->
```r
head(E_results_metastats)
```
<!-- rnb-source-end -->
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</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jIG1lcmdlIHRvcC50YWJsZXMgZnJvbSBIUzIwMDggYW5kIDIwMTIgLSBhbGwgZ2VuZXMgdGhhdCB3ZXJlIG5vdCBvcmlnaW5hbGx5IGZpbHRlcmVkIG90dS5cbmRpbSh0b3AudGFibGVfRWNvMDgpXG5gYGAifQ== -->
```r
# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_Eco08)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEzMDY3ICAgICA3XG4ifQ== -->
```
[1] 13067 7
```
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKHRvcC50YWJsZV9FY28xMilcbmBgYCJ9 -->
```r
dim(top.table_Eco12)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0NDMxICAgICA3XG4ifQ== -->
```
[1] 14431 7
```
<!-- rnb-output-end -->
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```r
top.table.E_All<- merge(top.table_Eco08,top.table_Eco12,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.E_All)
```
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0NjAxICAgIDEzXG4ifQ== -->
```
[1] 14601 13
```
<!-- rnb-output-end -->
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```r
#merge original results with results from meta-analysis
Ecotype_results <- merge(top.table.E_All,FC.selecDE, all.x = TRUE)
Ecotype_results2 <- merge(Ecotype_results, E_results_metastats, all.x = TRUE)
head(Ecotype_results2)
```
<!-- rnb-source-end -->
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</div>
<!-- rnb-frame-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxud3JpdGUudGFibGUoRWNvdHlwZV9yZXN1bHRzMiwgZmlsZSA9IFwiT3V0cHV0RmlsZXMvTWV0YV9FY290eXBlLnR4dFwiLCByb3cubmFtZXMgPSBGLCBzZXAgPSBcIlxcdFwiLCBxdW90ZSA9IEYpXG5gYGAifQ== -->
```r
write.table(Ecotype_results2, file = "OutputFiles/Meta_Ecotype.txt", row.names = F, sep = "\t", quote = F)
```
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Make summary tables and Upset plot
Ecotype as main effect
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgTWFrZSBhIHRhYmxlIG9mIHJlc3VsdHNcbmZpc2hjb21iX2RlIDwtIHJvd25hbWVzKGtlZXBERSlbd2hpY2goa2VlcERFWyxcIkRFLmZpc2hlcmNvbWJcIl09PTEpXVxuaW52bm9ybV9kZSA8LSByb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS5pbnZub3JtXCJdPT0xKV1cbmluZHN0dWR5X2RlIDwtIGxpc3Qocm93bmFtZXMoa2VlcERFKVt3aGljaChrZWVwREVbLFwiREUuMjAwOERhdGFzZXRcIl09PTEpXSxyb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS4yMDEyRGF0YXNldFwiXT09MSldKVxuSURELklSUihmaXNoY29tYl9kZSxpbmRzdHVkeV9kZSlcbmBgYCJ9 -->
```r
## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)
```
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<!-- rnb-output-begin eyJkYXRhIjoiICAgREUgICBJREQgIExvc3MgICBJRFIgICBJUlIgXG4xOC4wMCAxMC4wMCAgMC4wMCA1NS41NiAgMC4wMCBcbiJ9 -->
```
DE IDD Loss IDR IRR
18.00 10.00 0.00 55.56 0.00
```
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSURELklSUihpbnZub3JtX2RlLCBpbmRzdHVkeV9kZSlcbmBgYCJ9 -->
```r
IDD.IRR(invnorm_de, indstudy_de)
```
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<!-- rnb-output-begin eyJkYXRhIjoiICAgREUgICBJREQgIExvc3MgICBJRFIgICBJUlIgXG4xMS4wMCAgNy4wMCAgNC4wMCA2My42NCA1MC4wMCBcbiJ9 -->
```
DE IDD Loss IDR IRR
11.00 7.00 4.00 63.64 50.00
```
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### Make Upset Plot Ecotype
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBFY290eXBlICAyMDA4XG4gICNTdWJzZXQgdGhlIHRvcC50YWJsZXMgdG8gb25seSBjb250YWluIHRoZSByb3dzIHdoZXJlIFwiYWRqLlAuVmFsIDw9MC4wNVxuVVBfRWNvMDhfdG9wIDwtIHRvcC50YWJsZV9FY28wOFt3aGljaCh0b3AudGFibGVfRWNvMDgkYWRqLlAuVmFsPD0wLjA1KSwgXVxuZGltKFVQX0VjbzA4X3RvcClcbmBgYCJ9 -->
```r
# Ecotype 2008
#Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Eco08_top <- top.table_Eco08[which(top.table_Eco08$adj.P.Val<=0.05), ]
dim(UP_Eco08_top)
```
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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDcgN1xuIn0= -->
```
[1] 7 7
```
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuICAjIGNvbnZlcnRpbmcgY29sdW1uIG9mIFwiR2VuZVwiIGluIGRhdGFmcmFtZSBjb2x1bW4gaW50byB2ZWN0b3IgYnkgcGFzc2luZyBhcyBpbmRleFxuRWNvdHlwZV9IUzIwMDggPSBVUF9FY28wOF90b3BbWydHZW5lJ11dXG4jICBIUzIwMTJcbiAgI1N1YnNldCB0aGUgdG9wLnRhYmxlcyB0byBvbmx5IGNvbnRhaW4gdGhlIHJvd3Mgd2hlcmUgXCJhZGouUC5WYWwgPD0wLjA1XG5VUF9FY28xMl90b3AgPC0gdG9wLnRhYmxlX0VjbzEyW3doaWNoKHRvcC50YWJsZV9FY28xMiRhZGouUC5WYWw8PTAuMDUpLCBdXG5kaW0oVVBfRWNvMTJfdG9wKVxuYGBgIn0= -->
```r
# converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_HS2008 = UP_Eco08_top[['Gene']]
# HS2012
#Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Eco12_top <- top.table_Eco12[which(top.table_Eco12$adj.P.Val<=0.05), ]
dim(UP_Eco12_top)
```
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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQgN1xuIn0= -->
```
[1] 4 7
```
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuICAjY29udmVydGluZyBjb2x1bW4gb2YgXCJHZW5lXCIgaW4gZGF0YWZyYW1lIGNvbHVtbiBpbnRvIHZlY3RvciBieSBwYXNzaW5nIGFzIGluZGV4XG5FY290eXBlX0hTMjAxMiA9IFVQX0VjbzEyX3RvcFtbJ0dlbmUnXV1cbiNNZXRhQW5hbHlzaXNcbiMjIEtlZXAgdGhlIGdlbmVzIGZyb20gdGhlIG1ldGEtYW5hbHlzaXMgdGhhdCBoYXZlIGludmVybm9ybSBhZGp1c3RlZCBwLXZhbHVlIDw9IDAuMDUgYW5kIHRoZSBsb2cgZm9sZCBjaGFuZ2UgaXMgZ29pbmcgaW4gdGhlIHNhbWUgZGlyZWN0aW9uIGluIGJvdGggYW5hbHlzZXNcblVQX01ldGFBIDwtIEVjb3R5cGVfcmVzdWx0czJbd2hpY2goRWNvdHlwZV9yZXN1bHRzMiRERS5pbnZub3JtX2Fkal9QPD0wLjA1KSwgXVxuVVBfTWV0YUIgPC0gVVBfTWV0YUFbd2hpY2goYWJzKFVQX01ldGFBJHNpZ25GQyk9PTEpLF1cbmRpbShVUF9NZXRhQilcbmBgYCJ9 -->
```r
#converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_HS2012 = UP_Eco12_top[['Gene']]
#MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- Ecotype_results2[which(Ecotype_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)
```
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<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDExIDIzXG4ifQ== -->
```
[1] 11 23
```
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<!-- rnb-source-begin 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 -->
```r
#converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_Meta = UP_MetaB[['Gene']]
listEcotype <- list(HS2008 = Ecotype_HS2008, HS2012 = Ecotype_HS2012, MetaAnalysis = Ecotype_Meta)
# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_Ecotype.pdf")
#make plot
upset(fromList(listEcotype), mainbar.y.label = "Number of Differentially Expressed Genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
dev.off()
```
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<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
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